SAA-C03 Questions and Answers

The question asks for the most cost savings while not affecting application performance, and it explicitly calls out both compute costs and storage costs increasing. The best answer is the option that optimizes both cost drivers without reducing capacity needed for peak performance. Option D accomplishes this by combining two well-established cost optimization mechanisms: a Compute Savings Plan for baseline compute usage and S3 Lifecycle policies for lower-cost storage of older receipt images.

A Compute Savings Plan is best applied to the steady, always-needed portion of compute capacity. In an Auto Scaling group, there is typically a predictable minimum footprint that runs continuously. Purchasing a Savings Plan for that baseline delivers substantial discounts compared with On-Demand pricing while keeping performance unchanged. For traffic spikes, continuing to use On-Demand Instances is operationally simple and preserves scaling behavior and customer experience. This avoids the risk that reducing minimum capacity could introduce slower scale-out or insufficient resources during sudden load.

On the storage side, raw receipt images are stored in Amazon S3, and many applications access the newest objects most frequently. Using S3 Lifecycle policies to transition objects after 30 days to lower-cost tiers (such as infrequent access or archival tiers as appropriate for retrieval needs) reduces storage spend without changing how new uploads are handled or processed. It also keeps the application fast for recent receipts, where performance typically matters most.

Options A and B move storage to EFS, which is generally more expensive than S3 for object storage patterns and does not align with “stored as objects.” Option C reduces the maximum Auto Scaling capacity, which can directly harm performance during demand surges. Therefore, D provides the largest cost reduction while preserving performance and scaling characteristics.

The right solution isAPI Gateway + Lambda + Amazon Comprehend + DynamoDB. AWS documents that Amazon Comprehend is the NLP service for detecting entities, sentiment, and key phrases, which matches the workload exactly. DynamoDB is a fully managed, distributed database designed for high-throughput workloads and single-digit millisecond performance at scale, making it a strong fit for a highly available application handling many concurrent requests. The other answers misuse services: Amazon Translate is for translation, Amazon Textract is for extracting text from documents, and ElastiCache is a cache rather than the durable primary database described in the question. This makes option A the only architecture that aligns with both the NLP functions and the scalable database requirement. (AWS Documentation)

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The most secure and manageable way to provide developers with temporary, least-privilege access is by using AWS IAM Identity Center (formerly AWS SSO). IAM Identity Center allows assigning IAM roles with scoped permissions based on the developer’s team role. This ensures no permanent credentials are required and minimizes risk.

Option B enables role-based access with centralized identity and access management, making it the most secure and scalable solution for managing developer permissions.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

To achieve resilience across multiple Availability Zones, the architecture should use services that natively provide Multi-AZ durability and failover with minimal manual intervention. For static website content, Amazon S3 is the natural choice because it stores objects durably and is designed for high availability; it also integrates well with common web delivery patterns. For the database, the requirement is explicitly Microsoft SQL Server and resilience across AZs.

An Amazon RDS for SQL Server Multi-AZ deployment (Option B) is designed to provide high availability by maintaining a standby replica in a different Availability Zone and performing automated failover in certain failure scenarios. This meets the Multi-AZ resiliency requirement while reducing operational overhead compared to self-managed database clustering on EC2.

Option A uses RDS Custom for SQL Server, which is intended for scenarios where you need OS-level and database-level access/customization. That typically increases operational responsibility and is not the simplest “resilient Multi-AZ” answer unless the scenario demands deep customization (it does not here). Options C and D propose using EBS Multi-Attach for static content, which is not a good fit for static website assets and adds complexity; Multi-Attach is limited and does not replace object storage semantics. Option D also adds substantial operational overhead by running and managing SQL Server across EC2 instances in multiple AZs, including patching, backups, and HA configuration.

Therefore, B best meets all requirements: S3 for static object content and RDS for SQL Server Multi-AZ for resilient, managed database high availability across Availability Zones.

The Requester Pays feature in Amazon S3 allows the bucket owner to configure the bucket so that the requester, rather than the bucket owner, pays for data transfer and request costs. This is ideal for sharing large datasets with the public or with other AWS accounts when you want to minimize your own data transfer expenses.

Reference Extract from AWS Documentation / Study Guide:

" Requester Pays buckets allow you to configure the bucket so that the requester instead of the bucket owner pays the cost of the request and the data download from the bucket. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Cost Management section.

Predictive scaling in EC2 Auto Scaling uses machine learning to analyze historical load and forecast future capacity needs, then pre-scales capacity before the demand occurs. It is specifically designed for recurring, cyclical workloads such as weekly batch jobs.

In this scenario:

The workload pattern is known and recurring (weekly scripted jobs).

The company explicitly does not want to manually analyze trends.

They need capacity to be ready 30 minutes before jobs start.

Predictive scaling lets you:

Set the metric (CPU utilization) and target (60%).

Configure the policy to pre-launch instances 30 minutes in advance, satisfying the requirement automatically with minimal operational overhead.

Dynamic scaling (Option A) reacts after CPU increases, not before.

Scheduled scaling (Option B) requires manual analysis and ongoing adjustment of capacity values.

EventBridge + Lambda (Option D) introduces unnecessary custom logic and still reacts after CPU reaches 60%, not 30 minutes before.

AWS best practices strongly recommend enabling MFA on the root user, but they also emphasize planning for MFA device loss. AWS allows the configuration of multiple MFA devices for the root user, which provides redundancy and ensures continued access in disaster scenarios. Option B directly addresses the requirement by enabling a backup authentication method without weakening security controls.

Adding multiple MFA devices ensures that if one device is lost, damaged, or unavailable, the organization can still authenticate securely using the secondary device. This approach maintains strong security while eliminating the risk of permanent root account lockout. It also avoids time-consuming recovery processes that require contacting AWS Support and proving account ownership.

Option A is not sufficient because IAM administrator accounts do not replace root access and cannot perform certain root-only actions. Options C and D are not feasible because new administrator accounts or policy changes cannot be made if root access is already lost.

Therefore, B is the correct and AWS-recommended solution to ensure uninterrupted, secure access to the root account while maintaining MFA protection.

AWSService Catalogis designed to allow organizations to manage a catalog of approved products (including AMIs) that users can deploy. By creating a portfolio that contains only EC2 instances launched with preapproved AMIs, the company can enforce compliance with the approved operating system and software for all EC2 instances. Service Catalog also streamlines the process of launching EC2 instances, reducing the lead time while ensuring that developers use only the approved configurations.

Option B (EC2 Image Builder): While EC2 Image Builder helps in creating and managing AMIs, it doesn ' t provide the enforcement mechanism that Service Catalog does.

Option C (EventBridge rule and Systems Manager script): This solution is reactive and involves more operational complexity compared to Service Catalog.

Option D (AWS Config rule): This option is reactive (it terminates non-compliant instances after launch) and introduces additional operational overhead.

AWS References:

AWS Service Catalog

EC2 Account Attributes: Amazon EC2 allows you to set account attributes to automatically encrypt new EBS volumes. This ensures that all new volumes created in your account are encrypted by default.

Configuration Steps:

Go to the EC2 Dashboard.

Select " Account Attributes " and then " EBS encryption " .

Enable default EBS encryption and select the default AWS KMS key or a customer-managed key.

Prevention of Unencrypted Volumes: By setting this account attribute, you ensure that it is not possible to create unencrypted EBS volumes, thereby enforcing compliance with security requirements.

Operational Efficiency: This solution requires minimal configuration changes and provides automatic enforcement of encryption policies, reducing operational overhead.

AWS Lambda with Amazon EventBridge Scheduler is the most cost-effective solution because the workload is short, periodic, and does not require a continuously running server. The Python script runs every 30 minutes and takes only about 20 seconds, so paying for EC2 capacity that remains mostly idle would waste money. Lambda charges based on requests and execution duration, which fits this intermittent workload well. EventBridge Scheduler can invoke the Lambda function on a recurring schedule. Provisioned concurrency is unnecessary because this is not a latency-sensitive user-facing workload. API Gateway is also unnecessary because the script does not need an HTTP endpoint. Spot Instances reduce EC2 cost but still add instance management and interruption risk.

When using an SQS queue as an event source for AWS Lambda, the visibility timeout of the SQS queue plays a critical role in preventing duplicate message processing.

" If your Lambda function doesn ' t process the message and delete it from the queue within the visibility timeout period, the message becomes visible again and can be processed again by the same or another function instance. "

— AWS Lambda with SQS

In this scenario, the third-party API may take up to 60 seconds to respond. Since the Lambda function is configured with a 65-second timeout, the visibility timeout of the queue must be greater than or equal to the maximum function execution time to avoid the same message being reprocessed.

Incorrect Options:

A: Memory allocation doesn’t impact duplicate message handling.

B: Timeout is already sufficient; increasing it further does not solve the core issue.

C: Delivery delay controls initial delivery delay, not reprocessing logic.

S3 Block Public Access only blocks public access—not explicitly allowed access.

An S3 bucket policy can explicitly permit access only from specific source IP addresses using the aws:SourceIp condition. This allows secure, direct HTTP access to the S3 object from known firewall IP addresses.

Static website hosting (Option A) requires the bucket to be public and is blocked by the enabled S3 Block Public Access setting.

CloudFront with OAC (Option C) is unnecessary and adds cost and complexity.

Lambda (Option D) introduces operational overhead and is not needed since S3 policies can enforce IP restrictions directly.

The correct answer isDbecause the company needsshared storagefor Amazon EC2 instances that are deployed acrossmultiple Availability Zones, and the application already relies onLinux file system permissionswithout requiring application code changes.Amazon EFSis a fully managed, elastic, shared file system that s upports theNFS protocol, which is commonly used by Linux-based applications. It can be mounted concurrently from multiple EC2 instances across multiple Availability Zones, making it the best fit for this requirement.

Amazon EFS preserves standard file system semantics and POSIX-style permissions, which means the company can continue using familiar Linux file permissions and ownership models. This allows the application to work without code modification. EFS is also highly available and durable because it is designed as a Regional service that stores data redundantly across Availability Zones.

Option A is incorrect because Amazon S3 is object storage, not a native shared POSIX file system. Mounting S3 does not provide the same file system behavior and permissions model required by the application. Option B is incorrect because instance store volumes are ephemeral and tied to individual EC2 instances, so they are not suitable for shared, durable storage across multiple instances. Option C is incorrect because Amazon EBS volumes are block storage volumes that are generally attached to a single instance and are not the best solution for shared multi-AZ file access.

AWS best practices recommendAmazon EFSwhen Linux applications require a shared file system, file permissions, and multi-instance access with minimal operational changes. Therefore,Amazon EFSis the most appropriate solution.

Option Censures dynamic scaling based on demand using a target tracking scaling policy, optimizing costs.

Option Aresults in over-provisioning, leading to higher costs.

Option Bincreases costs by using larger instances.

Option Dis not feasible as instance store volumes are ephemeral and unsuitable for shared storage like EFS.

Since the company already usesIAM Identity Center, the lowest-overhead and most consistent design is to manage access throughpermission setsassigned to groups. AWS documentation explains that permission sets are the central mechanism IAM Identity Center uses to grant least-privilege access to AWS accounts and resources. This approach fits team-based access for development, testing, and operations without having to manage individual IAM users in each account. It also aligns with AWS guidance to apply least privilege by creating restrictive permission sets instead of broad standing permissions. The other options either introduce more manual identity management or create unnecessary account complexity for the scenario described. (AWS Documentation)

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Amazon SQS FIFO queues support deduplication and ordering. AWS documents thatMessageDeduplicationIdis used with FIFO queues to prevent duplicate delivery within a5-minute deduplication window, which matches this requirement exactly. Standard queues do not provide this deduplication guarantee, and SNS filter policies are not a deduplication mechanism for this use case. Because the company wants the least development effort, using API Gateway to place messages directly on an SQS FIFO queue with a deduplication token is the cleanest approach.

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To restrict S3 access to a specific VPC, AWS documentation specifies using a Deny policy with aws:SourceVpc for all unmatched VPCs:

Deny if aws:SourceVpc != vpc-11aabb22

This ensures only that VPC can reach the bucket via a VPC endpoint.

Allow policies (Options B and C) are incorrectly structured or use the wrong condition logic.

Option D limits by account, not VPC, and does not enforce VPC-level access.

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Amazon CloudFront is a content delivery network (CDN) service that caches copies of your content at edge locations around the world. This helps improve performance by serving content from the edge nearest to the user. However, when the content in Amazon S3 (your origin) is updated, those updates may not immediately reflect on the website if they are cached at the CloudFront edge locations.

The issue described in the question suggests that the CI/CD pipeline is functioning correctly, and updates are being deployed to S3. However, since CloudFront caches this content, the edge locations may still be serving outdated content, causing the updates to not be reflected on the website.

To resolve this issue, you need to invalidate the CloudFront cache. By invalidating the cache, CloudFront will remove the outdated content and retrieve the latest version from the S3 origin.

AWS documentation on this process:

CloudFront cache invalidation allows you to clear items from the cache so that CloudFront retrieves the latest version from the origin. You can create invalidation requests via the AWS Management Console, AWS CLI, or SDKs.

AWS CloudFront Documentation

Why the other options are incorrect:

A. Add an Application Load Balancer: ALBs are used to distribute incoming application traffic and are not relevant to caching or serving content from CloudFront.

B. Add Amazon ElastiCache for Redis or Memcached: This would help in caching database queries but has no relation to static website content hosted on CloudFront and S3.

D. Use AWS Certificate Manager (ACM): ACM is used for managing SSL/TLS certificates and is unrelated to the issue of content not being updated on CloudFront.

AWS Lake Formation provides fine-grained (column-level) access control for data stored in S3. Using a Lake Formation blueprint ensures database ingestion is automated and governed.

QuickSight can query Athena, and Athena honors Lake Formation permissions, enforcing column-level controls automatically.

Options A, B, and C rely on manual filtering or IAM policies, which cannot enforce column-level authorization for SQL queries.

To improve scalability and availability, EC2 Auto Scaling across multiple Availability Zones with an Application Load Balancer ensures resilient infrastructure. Migrating to Amazon Aurora MySQL with reader endpoints reduces read latency by offloading read traffic to replicas in otherAZs, while also increasing high availability.

The problem is initialization latency from launching instances based on a large AMI. Amazon EBS Fast Snapshot Restore is specifically designed to eliminate the added latency that normally occurs when volumes are created from snapshots and blocks are initialized lazily. By enabling FSR for the snapshot used by the AMI, the company can launch instances with fully initialized volumes more quickly, which helps the Auto Scaling group react faster to sudden demand. The other options do not directly solve the snapshot-initialization delay. Therefore, enabling Fast Snapshot Restore is the best performance optimization for this scenario.

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Amazon S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves data between frequent and infrequent access tiers based on usage. This tier offers immediate access to all objects, regardless of which tier they are stored in, while optimizing storage costs. S3 Intelligent-Tiering also provides the same high durability, availability, and security as other S3 storage classes and supports access from external resources using standard S3 APIs. Lifecycle policies and Glacier classes are more suitable for archival when infrequent access is predictable, but retrieval from Glacier classes is not immediate and incurs extra charges and delays.

Reference Extract from AWS Documentation / Study Guide:

" S3 Intelligent-Tiering is designed to optimize costs by automatically moving data between two access tiers when access patterns change. Data is always available and immediately accessible, making it ideal for unknown or unpredictable access patterns. "

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Storage Classes section.

Amazon Aurora PostgreSQL with Aurora Global Database is the strongest fit because it supports low-latency reads in secondary Regions and provides fast disaster recovery for a globally available database architecture. Migrating from PostgreSQL on EC2 to Aurora PostgreSQL reduces database management overhead and improves resilience compared with self-managed EC2. Aurora Global Database is designed for cross-Region replication with very low replication lag and fast secondary Region promotion, which supports the required RTO and RPO of less than 5 minutes. AWS Backup restoration is too slow for this recovery objective. Elastic Disaster Recovery protects the EC2 instance but does not address query latency as effectively. RDS cross-Region read replicas with manual promotion are less suitable for strict RTO/RPO targets.

AWS Step Functions provides a low-code, fully managed workflow service with a visual interface to orchestrate AWS Glue jobs in sequence. Step Functions integrates natively with AWS Glue and can be triggered by Amazon EventBridge based on events or schedules.

AWS Documentation Extract:

“AWS Step Functions makes it easy to coordinate multiple AWS services into serverless workflows so you can build and update apps quickly. Step Functions provides visual workflows and integrates with AWS Glue for ETL orchestration.”

(Source: AWS Step Functions documentation)

A: Manual scripting and dashboards do not provide a low-code or integrated visual workflow.

C: QuickSight is for BI, not workflow visualization.

D: ECS adds unnecessary complexity.

AWS X-Ray is the AWS service designed for distributed tracing, giving end-to-end visibility into how requests flow through microservices, APIs, and serverless components. It provides service maps, latency breakdowns, and performance insight per service, which is exactly what is required.

To use X-Ray effectively, each application stack must be instrumented. For infrastructure deployed through AWS CloudFormation, best practice is to enable and configure X-Ray in the CloudFormation templates (for Lambda, API Gateway, ECS, etc.). That way, all teams and stacks have consistent tracing enabled as part of their IaC.

Control Tower (Option B) cannot “turn on” X-Ray tracing inside applications; it only helps with account setup and guardrails.

CloudTrail (Option A) is for audit logging of API calls, not performance tracing.

CloudWatch (Option C) provides metrics and logs, but not request-level distributed tracing across multiple services.

Amazon Timestreamis purpose-built for storing and analyzing time-series data like telemetry.

Option Aleverages Timestream, SageMaker for ML, and QuickSight for visualization, meeting all requirements with minimal complexity.

Option Binvolves more complex DynamoDB-EMR integration.

Option Cuses Neptune, which is designed for graph databases, not telemetry data.

Option Dincorrectly uses Athena for visualization instead of QuickSight.

The correct solution isMulti-AZ deploymentbecause this is the Amazon RDS feature designed for high availability and fast failover. AWS prescriptive guidance shows Multi-AZ deployment as an in-Region DR option with an approximateRTO of 1–2 minutes, which is comfortably within the less-than-5-minute requirement. Read replicas are mainly for scaling reads, while snapshots and CloudFormation are not fast failover mechanisms. For a critical database workload that must remain highly available with minimal recovery time, Multi-AZ is the standard AWS-managed answer.

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AmazonEventBridgeAPI destinations allow you to send data from AWS to external systems, like Salesforce, using HTTP APIs, including those secured with OAuth. This provides a secure and scalable solution for sending messages from the order processing application to Salesforce.

Option A and B (SNS): SNS is not ideal for OAuth-secured external APIs and lacks the necessary OAuth integration.

Option D (MSK): Amazon MSK is a Kafka-based streaming solution, which is overkill for simple message forwarding to Salesforce.

AWS References:

Amazon EventBridge API Destinations

The correct answer isCbecause storing large binary objects such as documents directly in a relational database increases database size, raises storage cost, and can degrade performance over time.Amazon S3is a more appropriate service for storing large unstructured objects like documents, images, and media files. The application can store the actual document files in S3 and keep only the related metadata, object keys, and references in the existing Oracle database.

This design is more cost-effective because S3 storage is generally less expensive than expanding high-performance relational database storage for BLOB content. It also improves database performance because the database no longer needs to manage the overhead of storing and retrieving large document payloads. The database can focus on transactional and relational data, which is the workload it is designed to handle best.

S3 also provides high durability, scalability, and resilience. These characteristics satisfy the requirement for a highly available and resilient solution while avoiding unnecessary expansion of the RDS storage layer. Keeping metadata in the existing database minimizes the application changes compared with a full database migration.

Option A is incorrect because Magnetic storage would significantly reduce performance and is not appropriate for this workload. Option B would improve performance, but it would be much more expensive and does not address the root cause of storing large BLOBs in a relational database. Option D is incorrect because moving the relational Oracle workload to DynamoDB would require a major redesign and is not the most cost-effective solution for the stated problem.

The best architectural pattern is to useAmazon S3 for document storageand the relational database for metadata and transactional records.

The correct answer isAbecause the legacy mainframe requires asynchronous RESTful APIand the new service takes approximately3 minutesto complete each request. Among the choices,Amazon API Gateway REST APIis the appropriate option for exposing a synchronous REST endpoint to the mainframe. API Gateway REST APIs support longer integration timeouts than HTTP APIs, which makes them more suitable for workloads that take an extended time to process. The REST API can invoke an AWS Lambda function to execute the stock price calculation and return the result synchronously.

Option B is incorrect becauseAPI Gateway HTTP APIshave a shorter integration timeout and are not the best fit for a request that takes around 3 minutes. Option C is incorrect becauseWebSocket APIsare not RESTful and do not meet the requirement for synchronous REST-based integration with the mainframe. Option D is incorrect becauseLambda function URLsdo not provide the same API management features and are not the best choice for a long-running synchronous enterprise integration requirement of this kind.

AWS design guidance favors API Gateway when exposing managed APIs to external consumers. In this case, theREST APIoption best matches the requirement for synchronous RESTful access while supporting the longer processing duration. It also provides a managed front door for authentication, throttling, and operational control if the company needs those features later.

One note of honesty: some exam versions treat the REST API timeout detail as the deciding factor here. That is the key reasonREST APIis preferred overHTTP APIfor this question.

Security groups are stateful, but they cannot explicitly deny traffic — only allow.

Network ACLs are stateless and support explicit deny rules.To prevent Application A from sending traffic to Application B, configure a deny outbound rule in the network ACL of Application A’s subnet to block traffic to Application B’s subnet.

“Unlike security groups, network ACLs support both allow and deny rules, enabling you to explicitly block traffic.”

— Network ACLs

This is the correct method to block outbound traffic between subnets.

The correct answer isCbecause the existing application usesNFS storagefor an image cache that isa few terabytes in size, and the company wants themost cost-effective cloud alternative.Amazon EFS One Zoneis a managed file storage service that supports theNFS protocol, which allows the application to continue using a familiar file-based interface with minimal architectural change. Because this is acache, not primary durable data, storing it in aOne Zonefile system is a cost-optimized choice compared with a Regional EFS deployment.

Amazon EFS is designed for shared file storage and can scale to multiple terabytes automatically. It avoids the operational overhead of managing EC2 instances and local disks for cache storage. It is also better aligned than object storage for workloads that already expect direct NFS semantics. The application can mount the EFS file system and use it as the cache backend without redesigning around object APIs or in-memory caching models.

Option A is incorrect becauseElastiCache Memcachedis an in-memory cache and is typically much more expensive for multi-terabyte cache data than file storage. Option B is incorrect because using EC2 instance store volumes creates significant management overhead and the cache would be lost whenever instances are terminated or replaced. Option D is incorrect becauseAmazon S3 Express One Zoneis object storage, not NFS storage, and would require application redesign and additional metadata management in DynamoDB.

AWS storage guidance recommends matching file-based workloads to managed file services. For a largeNFS-based cache,Amazon EFS One Zoneis the most cost-effective and operationally simple solution.

When using imported key material with AWS KMS, you maintain control over the key lifecycle. AWS KMS allows you to import new key material into an existing KMS key (of type " external " or " imported " ), thus rotating the key material without changing the key ID or ARNs. This enables applications to continue using the same key for cryptographic operations without disruption.

You can also set an expiration time for the old key material, after which AWS KMS deletes the old material and requires new key material to be imported, enforcing regular rotation per your compliance requirements.

AWS Documentation Extract:

" To rotate imported key material, you can re-import new key material into the same KMS key. This retains the same key ID and ARNs so applications are unaffected. You can set an expiration time for imported key material and replace it as needed, ensuring compliance with your rotation policy. "

(Source: AWS Key Management Service Developer Guide, Importing Key Material, Rotating Key Material)

Other options:

A: You cannot create a new KMS key with the same key ID as an existing one.

B: Deleting and recreating the key disrupts application access because the key ID changes.

D: Automatic rotation is only available for AWS-managed keys, not for imported key material.

The most cost-effective and low-maintenance solution to aggregate S3 data from multiple accounts within the same AWS Region is to use Amazon S3 Same-Region Replication (SRR).

From AWS S3 Documentation:

" S3 Same-Region Replication (SRR) automatically replicates new objects between buckets in the same AWS Region. SRR is commonly used to aggregate logs into a single bucket, simplify data aggregation, and meet compliance requirements. "

(Source: Amazon S3 User Guide – Replication Overview)

Key benefits of using SRR for this use case:

Zero operational overhead – No Lambda, scripts, or custom code

Fully managed – AWS handles all replication automatically

Secure and efficient – No data leaves the us-west-2 Region

Supports cross-account replication – With proper IAM roles and permissions

Low-cost – Compared to custom ETL pipelines or Lambda solutions

In contrast:

Option A (Lifecycle policies) do not support copying, only expiration, transitions, or deletions.

Option C (scripts) requires custom scheduling and maintenance, increasing operational overhead.

Option D (Lambda) adds complexity and cost and is not as scalable or hands-off as SRR.

Elastic Beanstalk traffic splitting is the best operationally efficient deployment option for testing a new application version with a controlled portion of live traffic. Traffic splitting is effectively a canary-style deployment that routes a defined percentage of production traffic to the new version while monitoring health before full rollout. This minimizes disruption because Elastic Beanstalk manages much of the deployment orchestration, health evaluation, and rollback behavior. Manually changing ALB listener rules adds unnecessary operational work and increases the chance of routing mistakes. Replacing all EC2 instances in the Auto Scaling group risks broader production impact. Lambda weighted aliases apply to Lambda functions and are not the correct deployment mechanism for an Elastic Beanstalk application running on EC2 behind an ALB.

Key Requirements:

Handle traffic spikes efficiently and reduce latency caused by cold starts.

Optimize costs during low traffic periods.

Analysis of Options:

Option A:

Provisioned Concurrency:Reduces cold start latency by pre-warming Lambda environments for the required number of concurrent executions.

AWS Application Auto Scaling:Automatically adjusts provisioned concurrency based on demand, ensuring cost optimization by scaling down during low traffic.

Correct Approach:Provides a balance between performance during traffic spikes and cost optimization during idle periods.

Option B:

Using EC2 instances with Auto Scaling introduces unnecessary complexity for a serverless architecture. It requires additional management and does not address the issue of cold starts for Lambda.

Incorrect Approach:Contradicts the serverless design philosophy and increases operational overhead.

Option C:

Setting a fixed concurrency level ensures performance during spikes but does not optimize costs during low traffic. This approach would maintain provisioned instances unnecessarily.

Incorrect Approach:Lacks cost optimization.

Option D:

Using EventBridge Scheduler for periodic invocations may reduce cold starts but does not dynamically scale based on traffic demand. It also leads to unnecessary invocations during idle times.

Incorrect Approach:Suboptimal for high traffic fluctuations and cost control.

AWS Solution Architect References:

AWS Lambda Provisioned Concurrency

AWS Application Auto Scaling with Lambda

A DynamoDB gateway VPC endpoint is the most secure and cost-effective option because it allows private access from the VPC to DynamoDB without requiring an internet gateway, NAT gateway, or public IP path. Gateway endpoints for DynamoDB use route table entries to direct traffic to the DynamoDB prefix list over the AWS network. Endpoint policies can further restrict access to only the required DynamoDB tables, which supports least privilege. Interface endpoints are available for DynamoDB, but they add hourly and data processing charges, so they are not the most cost-effective choice when a gateway endpoint satisfies the requirement. VPC peering is not valid because DynamoDB is an AWS managed service, not a customer VPC that can be peered.

AWS Backup is the most appropriate solution for managing backups with minimal operational overhead while meeting the regulatory requirement to retain data for 90 days and enabling point-in-time restore for up to 14 days.

AWS Backup: AWS Backup provides a centralized backup management solution that supports automated backup scheduling, retention management, and compliance reporting across AWS services, including Amazon RDS. By creating a backup plan, you can define a retention period (in this case, 90 days) and automate the backup process.

Point-in-Time Restore (PITR): Amazon RDS supports point-in-time restore for up to 35 days with automated backups. By using AWS Backup in conjunction with RDS, you ensure that your backup strategy meets the requirement for restoring data to a specific point in time within the last 14 days.

Why Not Other Options?:

Option A (RDS Automated Backups): While RDS automated backups support PITR, they do not directly support retention beyond 35 days without manual intervention.

Option B (Manual Snapshots): Manually creating and managing snapshots is operationally intensive and less automated compared to AWS Backup.

Option C (Aurora Clones): Aurora Clone is a feature specific to Amazon Aurora and is not applicable to Amazon RDS for Oracle.

AWS References:

AWS Backup- Overview of AWS Backup and its capabilities.

Amazon RDS Automated Backups- Information on how RDS automated backups work and their limitations.

Spot Instances are the best pricing model because the jobscan tolerate interruptions, and the company wants the most cost-effective option. AWS recommends theprice-capacity-optimizedstrategy for Spot because it selects pools that balancelow pricewithlower interruption risk. Using multiple instance types in the Auto Scaling group also helps the company stay flexible enough to adopt newer generations over time. Reserved Instances do not fit as well because they reduce flexibility and do not align with the requirement to keep shifting toward the latest instance generations each year. Capacity-optimized alone minimizes interruption risk, but AWS recommends price-capacity-optimized when you want both lower interruption rates and better prices. Therefore, Spot with price-capacity-optimized allocation is the strongest answer.

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AWS recommends using Amazon ElastiCache as an in-memory caching layer in front of relational databases such as Amazon RDS to offload read traffic and significantly improve performance for read-heavy workloads. ElastiCache (Redis OSS) provides microsecond latency and can cache the results of frequent or expensive queries without requiring any change to the underlying database schema or engine. This directly addresses the requirement to improve performance without changing the database structure and with minimal operational overhead, because ElastiCache is a fully managed service (patching, failure detection, replacement, etc., are handled by AWS).

Option A (DynamoDB) would require a full data migration and application changes, including schema and query rewrites.

Option C (Memcached on EC2) introduces additional management overhead for EC2 instances (scaling, patching, HA).

Option D (DAX) is a caching layer only for DynamoDB and cannot be used directly with Amazon RDS.

AWS WAF (Web Application Firewall): AWS WAF allows you to create custom rules to block or allow web requests based on conditions that you specify.

Web ACL (Access Control List):

Create a web ACL and associate it with the ALB.

Use IP rule sets to specify the IP addresses of the retail locations that are allowed to access the application.

Security and Flexibility:

AWS WAF provides a scalable way to manage access control, ensuring that only traffic from registered IP addresses is allowed.

You can dynamically update the IP rule sets to add or remove IP addresses as needed.

Operational Simplicity: Using AWS WAF with a web ACL is straightforward and integrates seamlessly with the ALB, providing an efficient solution for managing access control based on IP addresses.

Amazon Kinesis is the right streaming service because it ingests and processes data in real time. For the user-data store, Amazon DynamoDB is the correct database because AWS documents that DynamoDB is a serverless, fully managed, distributed NoSQL database with single-digit millisecond performance at any scale. That directly matches the durability, low-latency, and unknown-scale requirements in the question. RDS is not the most natural fit for unpredictable massive serverless scaling here, while CloudFront and Global Accelerator solve delivery and network-edge problems rather than real-time event ingestion plus low-latency persistence. For a serverless analytics application with streaming game data and highly scalable user storage, Kinesis and DynamoDB are the best pair.

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Express Workflows in AWS Step Functions are optimized for high-throughput, short-duration, and low-cost workflows. They are suitable for applications with large volumes of parallel and interdependent tasks. When paired with HTTP APIs from API Gateway, which are more cost-efficient than REST APIs, this setup offers scalability and cost-effectiveness.

This design includes:

ALB: Supports AWS WAF integration.

Auto Scaling Group: Automatically scales based on load.

Multi-AZ Deployment: Increases resiliency and availability.

AWS WAF: Can be attached to ALB for application-layer protection.

“ALB is integrated with AWS WAF. You can deploy your EC2 instances in an Auto Scaling group across multiple Availability Zones to ensure high availability.”

— High Availability with Auto Scaling and ALB

Why not others?

A: Single AZ = not resilient

C: AWS WAF cannot attach to Route 53

D: NLB is not supported by AWS WAF

A & B. GuardDuty:Designed for threat detection, not for identifying or classifying sensitive data in S3 buckets.

C. Macie with EventBridge + SNS:Automatically identifies sensitive data, triggers alerts, and uses SNS for immediate notification via email.

D. Macie with EventBridge + SQS:Introduces latency due to periodic polling and adds unnecessary complexity.

Option B (Amazon FSx for Lustre): FSx for Lustre is optimized for high-performance file systems required by HPC workloads, scaling to millions of IOPS and supporting parallelized data access.

Option E (Cluster Placement Group with Auto Scaling): A cluster placement group ensures low-latency communication between EC2 instances, critical for HPC workloads. Amazon EMR simplifies running large-scale analytics jobs.

Amazon FSx for Lustre Documentation,AWS Placement Groups Documentation

This question focuses on scalability, operational overhead, and performance during unpredictable workloads.

API Gateway + AWS Lambda enables serverless compute, which scales automatically based on the number of requests. It requires no provisioning, maintenance, or patching of servers — eliminating operational overhead.

Amazon DynamoDB is a fully managed NoSQL database optimized for high-throughput workloads with single-digit millisecond latency.

Amazon S3 is designed for high availability and durability, and is ideal for storing unstructured content such as user-uploaded images.

By leveraging these fully managed and scalable services, the architecture meets the requirement of supporting rapid user growth while minimizing operational complexity. This solution aligns with the Performance Efficiency and Operational Excellence pillars in the AWS Well-Architected Framework.

???? References:

Serverless Web Application Architecture

Using DynamoDB with Lambda

Best Practices for API Gateway

AWS guidance is to cover steady baseline with commitments (Reserved Instances or Savings Plans) and use EC2 Spot Instances for burst capacity to minimize cost. Spot provides up to 90% discounts and is well-suited to fault-tolerant, queue-based workloads; interruptions are handled by replacing capacity automatically while messages remain durably in SQS. Using only Spot (A) risks capacity gaps; only RIs sized for peak (B) wastes cost during low demand. Option D scales on backlog but uses On-Demand for bursts, which is more expensive than Spot. With C, baseline capacity (RIs) keeps processing continuously (no downtime), and Spot adds cost-efficient throughput during spikes, aligning with Well-Architected cost and reliability patterns for queue workers.

The correct answers areA, C, and Ebecause the company needs a fully managed solution forSFTP uploads to Amazon S3, followed byautomatic decryptionandmovement of files to another directorywithminimal operational overhead.AWS Transfer Familyis the best fit because it provides a managed SFTP endpoint directly integrated with Amazon S3. Configuring the S3 bucket as the home directory enables customers to upload files without the company needing to manage its own file transfer servers.

The requirement to avoid separate authentication services and minimize operational work is well served by nativeAWS Transfer Family workflows. A workflow can automatically run post-upload steps on files as they arrive. TheDECRYPTaction is specifically designed to decrypt uploaded encrypted files as part of the managed workflow. After decryption, theCOPYaction can place the processed file into the second directory in the same S3 bucket for downstream processing.

Option B is less appropriate because using S3 events and Lambda adds custom orchestration where a native Transfer Family workflow already handles the need more simply. Option D is incorrect because polling with an external script introduces unnecessary infrastructure and operational overhead. Option F is also incorrect because AWS Batch polling and custom decryption logic is far more complex than a managed file-transfer workflow.

AWS best practices favor managed services and native workflow features to reduce custom code and infrastructure management. Therefore, the best solution is to useAWS Transfer Family for SFTP uploads, along withTransfer Family workflow DECRYPTandCOPYactions to automate the full post-upload process.

Amazon Cognito is the AWS-native user authentication service for web and mobile applications, andLambda@Edgecan run authorization logic close to users at CloudFront edge locations. AWS documentation notes that CloudFront can invoke Lambda@Edge functions at edge locations and that Cognito integrates well for user authentication scenarios. This combination fits the requirements for aserverlessauthorization/authentication architecture, low login latency, andglobal web content delivery. The other options either use services that are not ideal for this end-user web-authentication pattern or do not provide the same edge-based global delivery model. (docs.aws.amazon.com)

The application experiences unpredictable traffic spikes, which exceed the provisioned read and write capacity of the DynamoDB table, resulting in throttling errors. This is a classic scenario where on-demand capacity mode is the most appropriate solution.

Option A is correct because DynamoDB on-demand capacity automatically scales to handle sudden increases in traffic without requiring capacity planning. This eliminates throttling during unpredictable spikes and removes the operational burden of managing RCUs and WCUs. On-demand mode is particularly well-suited for workloads with variable or spiky access patterns.

Option B is incorrect because DynamoDB does not support read replicas in the same way as relational databases. Option C reduces cost for predictable workloads but does not prevent throttling if capacity limits are exceeded. Option D increases read cost and does not address capacity constraints.

Therefore, A is the best solution to improve resilience, eliminate throttling errors, and ensure a smooth user experience during traffic surges.

AWS documents that Amazon Macie supportsautomated sensitive data discovery, which continuously discovers and reports sensitive data in S3. AWS also documents that Macie findings can be processed automatically by usingAmazon EventBridge, including sending those events to a Lambda function for remediation or downstream processing. That combination provides continuous monitoring with low operational overhead and avoids the custom scheduling and job-launch logic in the other options. Because the question explicitly asks for continuous monitoring and low overhead, Macie automated sensitive data discovery plus EventBridge-triggered Lambda remediation is the best fit.

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Amazon Aurora Serverless is a cost-effective, on-demand, autoscaling configuration for Amazon Aurora. It automatically adjusts the database ' s capacity based on the current demand, which is ideal for workloads with variable and unpredictable usage patterns. Since the application is expected to be read-heavy with occasional writes and steady growth, Aurora Serverless can provide the necessary performance without requiring the management of database instances.

Cost-Optimization: Aurora Serverless only charges for the database capacity you use, making it a more cost-effective solution compared to always running provisioned database instances, especially for workloads with fluctuating demand.

Scalability: It automatically scales database capacity up or down based on actual usage, ensuring that you always have the right amount of resources available.

Performance: Aurora Serverless is built on the same underlying storage as Amazon Aurora, providing high performance and availability.

Why Not Other Options?:

Option A (RDS with Provisioned IOPS SSD): While Provisioned IOPS SSD ensures consistent performance, it is generally more expensive and less flexible compared to the autoscaling nature of Aurora Serverless.

Option C (DynamoDB with On-Demand Capacity): DynamoDB is a NoSQL database and may not be the best fit for applications requiring relational database features.

Option D (RDS with Magnetic Storage and Read Replicas): Magnetic storage is outdated and generally slower. While read replicas help with read-heavy workloads, the overall performance might not be optimal, and magnetic storage doesn’t provide the necessary performance.

AWS References:

Amazon Aurora Serverless- Information on how Aurora Serverless works and its use cases.

Amazon Aurora Pricing- Details on the cost-effectiveness of Aurora Serverless.

Stopping the DB instance is the most cost-effective option among the choices because it preserves the database for later restart without continuously paying for compute. AWS documents that a stopped RDS DB instance retains its metadata and can be restarted when needed, and that stopped instances automatically restart after 7 days. In practice, this means the company can periodically stop and restart the instance as required for short testing windows. The other options introduce unnecessary rebuild work or architecture changes. While the question wording includes both stop-db-cluster and stop-db-instance, the practical intent is clearly to stop the RDS MySQL instance temporarily and restart it when needed.

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Amazon ECS is the lowest-overhead choice because it avoids the control-plane and node-management work of EKS or raw EC2 while still supporting highly available multi-AZ placement. AWS ECS guidance notes that the service scheduler canspread tasks across Availability Zones, and target tracking scaling is a standard way to scale ECS services based on demand. Setting a minimum capacity of 3 ensures at least one task can remain in each Availability Zone, improving resilience. The EC2 and EKS options require more infrastructure management, and Lambda is not the right fit for generic containerized application workloads. Therefore, ECS with spread placement across AZs and service auto scaling is the best answer.

Because the application is public and serves global customers with dynamic IP addresses, the web tier must allow HTTPS traffic from0.0.0.0/0on port 443. For the database tier, the secure design is to allow MySQL access only from theweb tier’s security group, not from the internet. AWS RDS documentation explains that you can specify another VPC security group as the source for the DB security group, which allows application servers to connect without exposing the database publicly. That makes A the correct configuration. Options B and C do not work well for global dynamic users, and D exposes the database to the internet.

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Analysis:

The solution must handle variable sales volumes, preserve transaction information, and store data in an Amazon Aurora database with minimal operational overhead. UsingAPI Gateway, AWS Lambda, and SQSis the best option because it provides scalability, reliability, and resilience.

Why Option A is Correct:

API Gateway: Serves as an entry point for transaction data in a serverless, scalable manner.

AWS Lambda: Processes the transactions and sends them to Amazon SQS for queuing.

Amazon SQS: Buffers the transaction data, ensuring durability and resilience against spikes in transaction volume.

Second Lambda Function: Processes messages from the SQS queue and updates the Aurora database, decoupling the workflow for better scalability.

Dead-Letter Queue (DLQ): Ensures failed transactions are logged for later debugging or reprocessing.

Why Other Options Are Not Ideal:

Option B:

Using an ALB with ECS on EC2 introduces operational overhead, such as managing EC2 instances and scaling ECS tasks.Not cost-effective.

Option C:

EKS is highly operationally intensive and requires Kubernetes cluster management, which is unnecessary for this use case.Too complex.

Option D:

Amazon Data Firehose and DMS are not designed for real-time transactional workflows. They are better suited for data analytics pipelines.Not suitable.

AWS References:

Amazon API Gateway:AWS Documentation - API Gateway

AWS Lambda:AWS Documentation - Lambda

Amazon SQS:AWS Documentation - SQS

Amazon Aurora:AWS Documentation - Aurora

Amazon DynamoDB provides single-digit millisecond performance at any scale and is fully managed to handle millions of catalog records. It is ideal for structured catalog data such as product metadata and scales seamlessly during high-traffic events like sales. Amazon S3 is optimized for storing unstructured large objects such as videos and manuals, with virtually unlimited scalability and high durability. Option A (RDS) would not handle massive scale or traffic spikes as efficiently. Option C overloads DynamoDB by forcing it to store large binary data, which is not its purpose. Option D (DocumentDB) is suitable for JSON-like documents but not optimal for storing large media files and would add operational complexity. Therefore, option B represents the best separation of structured and unstructured data storage.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirements “shard-level control” and “custom checkpointing” point directly to the Kinesis Client Library (KCL), which is designed for building consumer applications that coordinate shard processing, perform load balancing across workers, and manage checkpoints in a durable store (commonly DynamoDB) with fine-grained control. While Lambda can consume from Kinesis Data Streams, its event source mapping abstracts shard coordination and checkpoint behavior; it is not the best fit when you explicitly need custom checkpointing logic and shard-level control beyond the managed integration.

Running the existing Lambda processing logic as a long-running consumer in Amazon ECS on AWS Fargate allows the company to operate a scalable KCL-based consumer fleet without managing servers. With Fargate, AWS manages the underlying compute while the application maintains direct control over shard assignment and checkpoint timing/frequency through KCL—exactly what the requirement calls for. Latency is minimized because the consumer reads directly from Kinesis Data Streams and processes records continuously, avoiding additional buffering layers or store-and-forward patterns.

Option A adds significant latency by delivering to S3 through Firehose and then triggering processing from S3 object notifications; this is optimized for delivery and batch-style processing, not low-latency streaming with shard-level control. Options B and D introduce SQS between Kinesis and processing, which adds another hop and does not inherently provide shard-level control or KCL-style checkpointing; FIFO also limits throughput and is not intended for high-scale streaming fan-in. Increasing Lambda provisioned concurrency can reduce cold starts, but it does not solve the need for custom checkpointing at the shard level.

Therefore, C best meets shard-level control and custom checkpointing requirements with the least latency by using a direct KCL consumer on a managed compute platform (Fargate).

EC2 Instance Connect Endpoint is the correct answer because it allows secure SSH access to private EC2 instances without public IP addresses, bastion hosts, NAT gateways, or inbound internet access. This directly satisfies the company’s restrictions and supports cost reduction by allowing the NAT gateways to be decommissioned if they are only used for this troubleshooting access pattern. Session Manager is not valid because the company does not support installing agents, and Session Manager requires the SSM Agent and Systems Manager connectivity. Bastion hosts require additional EC2 infrastructure and inbound access controls. Client VPN adds VPN management and does not remove the need for routing design across all VPCs as cleanly as EC2 Instance Connect Endpoint.

AWS S3 Multi-Region Access Points enable customers to use a single global endpoint for S3 bucket access across multiple AWS Regions, providing automatic routing to the nearest Region. This reduces public network congestion by directing user data to the closest S3 bucket and supports high availability with active-active configuration.

Cross-Region Replication ensures data is replicated between buckets in different Regions, meeting the failover and resilience requirements with minimal management overhead.

Option D aligns best with AWS’s recommended approach to resilient, low-latency, and simplified multi-Region S3 access.

Option A lacks the global endpoint and automatic failover. Option B incorrectly describes Multi-Region Access Points configuration and suggests global endpoints per Region, which is contradictory. Option C’s cross-account replication adds complexity and does not provide a single global endpoint.

Option Auses an IAM role attached to the EC2 instance profile, enabling secure and automated access to Secrets Manager. This is the recommended approach.

Option Buses IAM users, which is less secure and harder to manage.

Option Cis not practical for accessing secrets programmatically.

Option Dviolates best practices by granting direct access to the EC2 instance.

Amazon SQS FIFO queuesensure that orders are processed in the exact order received and maintain message deduplication.

AWS Lambdascales automatically, handling bursts and maintaining high availability in a cost-effective manner.

Option A and D:Amazon SNS does not guarantee ordered processing.

Option C:Standard SQS queues do not guarantee order.

AWS Documentation References:

Amazon SQS FIFO Queues

A VPC gateway endpoint for Amazon S3 enables private connectivity to S3 without routing traffic through a NAT gateway or over the internet, eliminating NAT gateway costs. This solution is secure and redundant, as S3 endpoints are highly available by design.

AWS Documentation Extract:

" A gateway VPC endpoint enables you to privately connect your VPC to supported AWS services without requiring a NAT gateway or internet gateway. "

(Source: Amazon VPC documentation, Gateway Endpoints)

A: NAT instances still incur operational overhead and costs.

B: Internet gateway exposes resources and does not provide private access.

D: Direct Connect is for hybrid networking, not for cost-efficient S3 access.

Amazon S3 Transfer Acceleration is designed specifically to speed up long-distance uploads and downloads to an S3 bucket by using globally distributed CloudFront edge locations and optimized AWS network paths. Because users are located worldwide and upload images to one centralized S3 bucket, Transfer Acceleration directly addresses the performance bottleneck. The important detail is that clients must use the S3 Accelerate endpoint; simply enabling Transfer Acceleration but continuing to use the standard endpoint would not provide the acceleration benefit. CloudFront is useful for caching and accelerating content delivery to users, but it is not the best answer for improving global upload speed into a centralized S3 bucket.

A Stored Volume Gateway stores the entire dataset locally on-premises while asynchronously and securely backing up the data to AWS. This meets the requirement for full local access and automatic transfer to AWS.

Cached Volume Gateway (Option C) keeps only frequently accessed data locally; the full dataset is stored in AWS, not on-premises.

Snowball and Snowball Edge (Options A and B) are migration tools, not ongoing backup solutions.

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This solution usesCloudFrontto serve the website securely over HTTPS usingAWS Certificate Manager (ACM)for SSL certificates.Origin Access Control (OAC)ensures that only CloudFront can access the S3 bucket directly.AWS WAFwith an IP set rule restricts access to the website, allowing only the on-premises IP address.Route 53is used to create an alias record pointing to the CloudFront distribution. This setup ensures secure, private access to the website with low administrative overhead.

Option A and B: S3 bucket policies and access points do not provide HTTPS support, nor do they offer the same level of security as CloudFront with WAF.

Option D: Signed URLs are more suitable for temporary, expiring access rather than a permanent solution for on-premises employees.

AWS References:

Amazon CloudFront with Origin Access Control

AWS Shield Advanced is designed to provide enhanced protection against DDoS attacks with proactive engagement and response capabilities, making it the best solution for this scenario.

AWS Shield Advanced: This service provides advanced protection against DDoS attacks. It includes detailed attack diagnostics, 24/7 access to the AWS DDoS Response Team (DRT), and financial protection against DDoS-related scaling charges. Shield Advanced also integrates with Route 53 and the Application Load Balancer (ALB) to ensure comprehensive protection for your web applications.

Route 53 and ALB Protection: By adding your Route 53 hosted zones and ALB resources to AWS Shield Advanced, you ensure that these components are covered under the enhanced protection plan. Shield Advanced actively monitors traffic and provides real-time attack mitigation, minimizing the impact of DDoS attacks on your application.

Why Not Other Options?:

Option A (AWS Config): AWS Config is a configuration management service and does not provide DDoS protection or detection capabilities.

Option B (AWS WAF): While AWS WAF can help mitigate some types of attacks, it does not provide the comprehensive DDoS protection and proactive engagement offered by Shield Advanced.

Option C (GuardDuty): GuardDuty is a threat detection service that identifies potentially malicious activity within your AWS environment, but it is not specifically designed to provide DDoS protection.

AWS References:

AWS Shield Advanced- Overview of AWS Shield Advanced and its DDoS protection capabilities.

Integrating AWS Shield Advanced with Route 53 and ALB- Detailed guidance on how to protect Route 53 and ALB with AWS Shield Advanced.

Amazon S3 Express One Zone provides single-digit millisecond latency and high throughput, making it ideal for ML workloads that require multiple compute nodes and fast access. It is also more cost-effective than traditional file or block storage for temporary, high-speed needs.

Regulatory compliance often requires immutability, meaning data cannot be altered or deleted by any user, including administrators, during a retention period. Amazon S3 Object Lock in compliance mode is specifically designed for this requirement.

Option B enforces a write-once-read-many (WORM) model. Once compliance mode is enabled and a retention period is set, no one—including root users—can delete or overwrite the objects until the retention expires. This provides the strongest level of protection and satisfies strict regulatory standards.

Option A (governance mode) allows privileged users to override the lock, which may not satisfy regulatory mandates. Option C does not prevent deletion or modification before retention expiration. Option D addresses storage cost, not immutability.

Therefore, B is the correct solution for regulatory-grade data protection.

Concurrency Scaling allows Amazon Redshift to add transient capacity automatically to handle bursts in concurrent queries. This is ideal for scenarios with predictable surge periods, such as end-of-month reporting.

It improves performance without manual resizing or cluster modification and without affecting availability. Resizing requires manual intervention and potential downtime, and Redshift Spectrum is designed for querying data in S3, not for solving concurrency issues.

AWSSecrets Manageris the best solution for securely storing and managing sensitive information, such as usernames and passwords. Secrets Manager provides automatic rotation, fine-grained access control, and encryption of credentials. It is designed to integrate easily with other AWS services, such as CloudFormation, to automate the retrieval of secrets via theBatchGetSecretValue API.

Secrets Manager has a lower operational overhead than manually managing credentials, and it offers features like automatic secret rotation that reduce the need for human intervention.

Option A and C (Parameter Store): While Systems Manager Parameter Store can store secrets, Secrets Manager provides more specialized capabilities for securely managing and rotating credentials with less operational overhead.

Option B and C (AWS Batch): Introducing AWS Batch unnecessarily complicates the solution. Secrets Manager already provides simple API calls for retrieving secrets without needing an additional service.

AWS References:

AWS Secrets Manager

Secrets Manager with CloudFormation

Amazon SQS is the AWS-recommended service for handling decoupled, scalable, ordered, durable message ingestion with potentially large workloads. It supports payloads up to 256 KB directly and up to several MB using S3-backed large-payload extensions.

Lambda functions can process messages from SQS with automatic scaling. ECS with the Fargate launch type provides a serverless container platform for the frontend, scaling based on demand without managing servers.

SNS (Options B and D) is not a queue, does not provide durable message buffering, and is not appropriate for variable, bursty workloads that may exceed backend throughput. Lambda@Edge (Options A and D) is unsuitable for backend order processing because it runs at CloudFront edge locations with strict limitations.

IAM Access Analyzeris the correct service because AWS documents that it helps identify and reviewunused accessfor IAM users and roles across AWS accounts and organizations. It can generate findings that show where permissions are broader than necessary and can recommend narrower policies when applicable. Network Access Analyzer focuses on network reachability, not IAM over-permission. CloudWatch alarms on user activity do not analyze granted permissions, and Amazon Inspector is not the service for IAM permission-rightsizing. Since the company wants the least administrative overhead across multiple accounts managed with AWS Organizations, IAM Access Analyzer is the AWS-native service purpose-built for this kind of access review. (AWS Documentation)

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The requirement that the security team manage key lifecycle actions such as creation, deletion, and rotation points tocustomer managed AWS KMS keys, not SSE-S3. Customer managed KMS keys allow fine-grained IAM authorization for both the Lambda execution role and the security administrator group. That supports least privilege because the Lambda role can be restricted to decrypt usage, while the administrator group can be limited to the KMS management actions it actually needs. The other answers either do not provide the required KMS management control or grant permissions too broadly. Therefore, using customer managed KMS keys with separate, scoped IAM permissions for decryption and key administration is the correct design.

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Using an Application Load Balancer (ALB) allows for integration with AWS WAF to inspect all incoming traffic. Running the application on Amazon ECS provides a scalable and managed container orchestration service. Utilizing Amazon Aurora Serverless for the PostgreSQL database offers automatic scaling based on application demand, which is cost-effective for workloads with variable traffic patterns, such as higher traffic on weekdays and lower traffic on weekends.

AWS best practice is to use IAM roles with instance profiles for EC2 instances so that applications obtain temporary credentials automatically and do not need to store access keys.

To honor the principle of least privilege, each microservice should have an IAM role that grants only the specific permissions it needs.

Therefore, creating individual IAM roles per microservice and attaching them via instance profiles (Option D) both minimizes long-term credential management and applies least privilege cleanly.

Why others are not correct:

A: Using IAM users with access keys in code is insecure and high-overhead (key rotation, secret management).

B: A single broad role violates least privilege because every microservice gets more permissions than it needs.

C: Separate accounts per microservice is extreme over-segmentation and significantly increases operational complexity.

This workload has two distinct needs: (1) a managed way for external clients to upload files using a familiar managed file transfer protocol, and (2) an automated, low-ops method to run a watermarking step and store the resulting images as objects. AWS Transfer Family provides a fully managed capability to receive files over protocols such as SFTP, while landing those files directly into Amazon S3 as objects. That directly satisfies the requirement that “uploaded images must be stored as objects” with minimal infrastructure management.

To automate watermarking with minimal operational overhead, invoking AWS Lambda is a strong fit. Lambda is serverless and scales automatically with incoming uploads, and it can run the watermarking logic as code without managing servers. Transfer Family supports managed workflows that can orchestrate post-upload actions; using a Transfer Family workflow to invoke a Lambda function provides a clean, managed, event-driven pipeline: clients upload via SFTP → objects land in S3 → workflow triggers watermark processing → output is written back to S3.

Option A adds operational burden by requiring an EC2-based application tier for watermarking, which increases patching and scaling responsibilities. Option B can work (S3 + Batch), but it requires building and operating the upload mechanism (REST APIs) and typically more orchestration than necessary for simple “upload then process” flows; Batch is better when you need large-scale, long-running batch compute rather than lightweight per-object processing. Option D is unsuitable because S3 Glacier Deep Archive is intended for long-term archival with slow retrieval, which conflicts with active workflow processing and watermark automation.

Therefore, C best meets the requirements using managed services end-to-end: Transfer Family for ingestion, S3 for object storage, and Lambda for automated watermarking.

Unpredictable Lambda traffic can create a “connection storm” against a relational database because each concurrent Lambda invocation may open its own database connection. Relational engines like PostgreSQL have finite connection capacity, and excessive connections can degrade performance, increase latency, and exhaust database resources. Amazon RDS Proxy is designed to address this by pooling and reusing database connections, multiplexing many application requests over a smaller number of established database connections. This helps keep database performance more predictable under spiky, highly concurrent workloads such as Lambda.

Because the database is a private RDS for PostgreSQL instance, the Lambda functions must run with network access to the VPC subnets and security groups that can reach the database and proxy. Therefore, deploying the Lambda functions inside a VPC and pointing the database client to the RDS Proxy endpoint is the correct operational pattern. RDS Proxy also provides benefits like improved failover behavior handling and centralizing credential management integration patterns (commonly with IAM authentication and/or Secrets Manager), further reducing operational risk.

Option A uses a custom endpoint, which does not solve the fundamental connection scaling problem; it’s not a connection pooler. Option C and D place Lambda outside the VPC, which will not work for a private database that has no public connectivity path. Even if connectivity were possible, D would still be incomplete because you’d need private networking to reach the proxy.

Therefore, B best meets the requirement by introducing managed connection pooling via RDS Proxy and ensuring private network reachability by running Lambda in the VPC.

AWS Well-Architected recommends multi-AZ, elastic architectures: use Auto Scaling groups across multiple Availability Zones for EC2 to eliminate single-AZ failure and automatically replace capacity. For relational databases, Amazon RDS Multi-AZ provides synchronous replication and automatic failover for high availability. Serving static content via Amazon CloudFront (with S3 origin) improves resiliency and performance by caching at edge locations and reducing origin load/latency. Options A and C concentrate compute in one AZ and lack resilient static delivery. Option D changes the stack unnecessarily and proposes EFS One Zone-IA for static assets, which is not multi-AZ and is intended for infrequently accessed, single-AZ workloads, reducing resilience. Therefore, B aligns with best practices for high availability, fault tolerance, and global performance.

AWS Transfer Family: This service provides fully managed support for file transfers directly into and out of Amazon S3 using the SFTP, FTPS, and FTP protocols.

SFTP Endpoints:

Set up an AWS Transfer Family server and configure SFTP endpoints to handle the file transfers.

This service is scalable and managed, reducing operational overhead compared to running an SFTP solution on EC2 instances.

Integration with Active Directory:

Choose the AWS Directory Service option as the identity provider for the Transfer Family server.

Use AD Connector to link the on-premises Active Directory with AWS, allowing employees to use their existing AD credentials to access the SFTP service.

Operational Efficiency: This solution leverages managed services for both file transfer and identity management, ensuring minimal changes to the current authentication mechanisms and reducing operational overhead.

Amazon DynamoDB supports Time to Live (TTL), which automatically and asynchronously deletes expired items based on a timestamp attribute. TTL is ideal for automatic data expiration with very low operational overhead.

In this scenario:

When a user requests deletion, the system can calculate an expiration timestamp one month in the future.

A Lambda function is used once per request to write or update the item’s TTL attribute to that timestamp (Option C).

DynamoDB TTL then automatically removes the item after the expiration time. Deletion typically occurs within 48 hours of the TTL timestamp, which satisfies the “within one month” requirement.

This approach offloads the actual deletion to DynamoDB and avoids building complex orchestration or periodic cleanup jobs.

Options A, B, and D add unnecessary components (EventBridge, streams, AWS Config, and Lambda-based deletion workflows) on top of TTL or custom logic, increasing operational overhead without providing additional value for the given requirement.

For an application requiring TCP communication and high availability:

Network Load Balancer (NLB)is the best choice for load balancing TCP traffic because it is designed for handling high-throughput, low-latency connections.

Auto Scaling groupensures that the application can automatically scale based on demand, adding or removing EC2 instances as needed, which is crucial for handling user growth.

Option C (Application Load Balancer): ALB is primarily for HTTP/HTTPS traffic, not ideal for TCP.

Option D (Manual scaling): Manually adding instances does not provide the automation or scalability required.

Option E (Gateway Load Balancer): GLB is used for third-party virtual appliances, not for direct application load balancing.

AWS References:

Network Load Balancer

Auto Scaling Group

Amazon Elastic File System (Amazon EFS) is a fully managed, elastic, shared file system that can be mounted concurrently by multiple EC2 instances across multiple Availability Zones. EFS automatically grows and shrinks as files are added or removed, providing seamless scalability for unpredictable and growing storage needs. It supports simultaneous access from multiple compute resources, making it ideal for applications where several EC2 instances must read and write to the same data set concurrently. EBS volumes cannot be shared across multiple Availability Zones, and S3 Glacier is intended for archival and infrequent access, not for active, hourly data analysis.

Reference Extract from AWS Documentation / Study Guide:

" Amazon EFS provides a scalable, fully managed elastic NFS file system for use with AWS Cloud services and on-premises resources. It can be mounted to many EC2 instances across multiple Availability Zones and is ideal for shared data scenarios. "

Source: AWS Certified Solutions Architect – Official Study Guide, Storage Solutions section; Amazon EFS User Guide.

AWS Lambda for Operational Efficiency:

AWS Lambdais a serverless compute service that allows you to run code without provisioning or managing servers. It automatically scales based on the number of invocations and eliminates the need to maintain and monitor EC2 instances, making it far more operationally efficient compared to running a cron job on EC2.

By using Lambda, you pay only for the compute time that your function uses. This is especially beneficial when dealing with lightweight tasks, such as scanning a DynamoDB table and sending email messages once a day.

Amazon DynamoDB:

DynamoDBis a highly scalable, fully managed NoSQL database. The table stores employee start dates, and scanning the table to find the employees who have a work anniversary on the current day is a lightweight operation. Lambda can easily perform this operation using theDynamoDB Scan APIor queries, depending on how the data is structured.

Amazon SNS for Email Notifications:

Amazon Simple Notification Service (SNS)is a fully managed messaging service that supports sending notifications to a variety of endpoints, including email. SNS is well-suited for sendingout email messages to employees, as it can handle the fan-out messaging pattern (sending the same message to multiple recipients).

In this scenario, once Lambda identifies employees who have their work anniversaries, it can use SNS to send the email notifications efficiently. SNS integrates seamlessly with Lambda, and sending emails via SNS is a common pattern for this type of use case.

Event Scheduling:

To automate this daily task, you can schedule the Lambda function usingAmazon EventBridge (formerly CloudWatch Events). EventBridge can trigger the Lambda function on a daily schedule (cron-like scheduling). This avoids the complexity and operational overhead of manually setting up cron jobs on EC2 instances.

Why Not EC2 or SQS?:

Option A & Bsuggest running a cron job on anAmazon EC2instance. This approach requires you to manage, scale, and patch the EC2 instance, which increases operational overhead. Lambda is a better choice because it automatically scales and doesn’t require server management.

Amazon Simple Queue Service (SQS)is ideal for decoupling distributed systems but isn’t necessary in this context because the goal is to send notifications to employees on their work anniversaries. SQS adds unnecessary complexity for this straightforward use case, whereSNSis the simpler and more efficient solution.

AWS References:

AWS Lambda

Amazon SNS

Amazon DynamoDB

Amazon EventBridge

Summary:

UsingAWS Lambdacombined withAmazon SNSto send notifications, and scheduling the function withAmazon EventBridgeto run daily, is the most operationally efficient solution. It leverages AWS serverless technologies, which reduce the need for infrastructure management and provide automatic scaling. Therefore,Option Cis the correct and optimal choice.

For the web tier, the company needs multi-AZ scaling and high availability, which is best achieved with anAuto Scaling group across multiple Availability Zones behind an Application Load Balancer. AWS fault-tolerance guidance recommends distributing EC2 instances across multiple AZs and using Auto Scaling to maintain balance and resilience. For the database tier, the question specifically requires reduced MySQL read latency, so anAurora MySQL cluster with a reader instance in a separate AZis the stronger fit. AWS Aurora guidance recommends distributing the primary and reader instances across Availability Zones and notes that Aurora Auto Scaling can add reader instances for handling increased connectivity and workload. Together, B and C deliver scalability, HA, and better read performance.

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When operating in disconnected or limited-connectivity environments, such as ships at sea, AWS recommends using AWS Snowball Edge devices to host local compute and storage workloads. Snowball Edge supports Amazon EC2 instances and AWS IoT Greengrass, and can run Amazon EKS Anywhere for local Kubernetes cluster deployments.

From AWS Documentation:

“You can use AWS Snowball Edge devices to run compute-intensive applications in remote or disconnected locations. Snowball Edge devices support running Amazon EC2 instances and Amazon EKS Anywhere clusters.”

(Source: AWS Snow Family – Developer Guide)

Why A is correct:

Snowball Edge provides local compute and storage even without internet connectivity.

Multiple Snowball Edge devices can be clustered together for high availability and failover.

Fully self-contained environment suitable for ships or field operations.

Why the others are incorrect:

B, C, D require network connectivity to AWS Regions or Zones (Local Zone, Outposts, Wavelength), which is not available at sea. These options cannot operate fully disconnected.

AWS CloudTrail Lakeis a fully managed service that allows the collection, storage, and querying ofCloudTrail eventsfor both AWS and non-AWS services. CloudTrail Lake can be customized to collect logs from various sources, ensuring a centralized audit solution. It also supports long-term storage, so logs can be retained for 7 years, meeting the compliance requirement.

Option A (Data Lake): Setting up a data lake in S3 introduces unnecessary operational complexity compared to CloudTrail Lake.

Option C (Ingest non-AWS services into CloudTrail): CloudTrail Lake is better suited for this task with less operational overhead.

Option D (CloudWatch Logs): While CloudWatch can store logs, CloudTrail Lake is specifically designed for API auditing and storage.

AWS References:

AWS CloudTrail Lake

Option Ais the simplest solution, using DynamoDB Streams and Lambda for real-time ingestion into S3.

Options B, C, and Dadd unnecessary complexity with Data Firehose or Kinesis.

Amazon RDS Performance Insights is a “database performance tuning and monitoring feature” that can be enabled with a few clicks and “helps you quickly assess the load on your database” and identify bottlenecks. It surfaces key metrics, including DB load, top SQL, waits, and host metrics such as CPU utilization, which are commonly used indicators for right-sizing (up or down). This provides the lowest operational effort compared to building log pipelines or querying CloudTrail (which does not capture SQL workload characteristics). AWS X-Ray traces application requests, not database internals, and CloudWatch Logs aggregation requires custom ingestion and analysis. Enabling Performance Insights directly on the RDS instance provides actionable utilization data to right-size with minimal setup.

Amazon SQS is the AWS service built to decouple producers and consumers and to absorb variable traffic. AWS documentation describes SQS as a fully managed message queue that enables decoupling and scaling of microservices and distributed systems. AWS Lambda guidance also uses SQS as the standard pattern when a downstream component may be slower than an upstream one, because the queue durably persists messages and decouples the services. That directly supports frontend responsiveness during traffic spikes. It also supports reprocessing because messages remain in the queue until consumers successfully process and delete them, and failed processing can be retried. The ALB and direct HTTPS options do not provide durable buffering. Kinesis is possible, but for decoupled order processing and retry-oriented workflows, SQS is the more natural and simpler service.

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Amazon S3 presigned URLs are the most secure fit because they providetime-limited access to specific objectswithout requiring the external vendor to have AWS credentials or direct bucket permissions. AWS documentation states that presigned URLs grant temporary access to private S3 objects and can be used for downloads through a browser or program. This is stronger than creating an IAM user or sharing temporary keys because those methods still expose AWS credentials to a third party. A bucket policy based only on source IP is broader and less precise than object-level, time-bounded access. Presigned URLs let the company control exactlywhich objectcan be downloaded andfor how long, which is exactly what the scenario requires. (AWS Documentation)

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To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory- Information on how to set up and use S3 Inventory.

S3 Batch Operations- Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

Amazon S3 Glacier Flexible Retrieval is designed for long-lived, infrequently accessed data where retrieval times of minutes to hours are acceptable. It offers very low storage cost per GB while still supporting faster retrieval than archival tiers that can take many hours or days.

The requirement states that data is rarely accessed and must be retrievable within 12 hours. Glacier Flexible Retrieval meets this retrieval-time requirement and is more cost-effective per GB than Standard and Infrequent Access storage classes for long-term, rarely accessed data.

S3 Standard, S3 Standard-IA, and S3 One Zone-IA are optimized for more frequent or lower-latency access and have higher storage cost per GB compared to Glacier Flexible Retrieval.

Amazon RDS Custom for Oracle: This service allows you to bring your own Oracle Database licenses and provides the flexibility to customize the database settings, making it suitable for applications that require privileged access and third-party database features.

BYOL (Bring Your Own License):

RDS Custom supports the BYOL model, allowing you to use your existing Oracle licenses and comply with licensing requirements.

This helps in leveraging existing investments and reducing migration costs.

Customization and Third-Party Features:

RDS Custom allows for deeper customization of the database environment compared to standard RDS instances.

This makes it possible to support the third-party features that your application relies on without significant changes.

Migration Process:

Use native Oracle tools like Data Pump or RMAN to migrate the database to RDS Custom.

Customize the database settings post-migration to ensure compatibility with third-party features.

AWS Global Accelerator provides static anycast IP addresses that are announced from AWS edge locations. Traffic sent to these IPs is routed over the AWS global network to the nearest healthy endpoint (for example, an ALB or NLB) in any Region. Because clients connect to static IPs instead of DNS names that change, failover does not depend on DNS TTLs or client cache expiry, which avoids stale DNS entries.

You can register endpoints in multiple Regions and let Global Accelerator automatically fail over when health checks fail, all behind a single global endpoint.

CloudFront (Option A) is primarily for HTTP/HTTPS content caching and still uses DNS; it is not intended as a generic multi-Region failover solution for arbitrary applications.

Route 53 (Options B and D) relies on DNS; clients can cache DNS responses and may keep sending traffic to an unhealthy Region until TTLs expire, which does not meet the “avoid stale DNS client caches” requirement.

The most resilient and scalable architecture for handling millions of high-resolution images with frequent updates is to store the binary images in Amazon S3 and store their metadata or reference (geographic code and S3 URL) in Amazon DynamoDB.

From AWS Documentation:

“Store large objects like images in Amazon S3 and use Amazon DynamoDB to store metadata and references. This design pattern is scalable, highly available, and cost-effective.”

(Source: AWS Architecture Blog – Best Practices for Handling Large Objects)

Why B is correct:

Amazon S3 is designed for storing large volumes of binary data (images).

DynamoDB provides low-latency reads/writes using the geographic code as the partition key.

Highly available, serverless, and auto-scaling, suitable for disaster scenarios with bursts of activity.

Reduces pressure on the database layer by separating metadata from image storage.

Why the others are incorrect:

Option A and D: Storing images directly in RDS is expensive, unscalable, and not optimal for binary storage.

Option C: DynamoDB is suitable, but storing actual binary image data in DynamoDB is not best practice due to item size limits (400 KB) and performance concerns.

AWS IAM Identity Center:

IAM Identity Centerprovides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) throughSAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Centeralso simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuringIAM Identity Centerto connect to the company ' s IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected,IAM Identity Centerhandles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separateIAM usersfor each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWSroot userswith synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D:AWS Resource Access Manager (RAM)is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn’t provide a solution for authentication via an external IdP.

AWS References:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting upIAM Identity Centerand connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore,Option Cis the best solution.

Amazon S3 Intelligent-Tiering automatically moves objects between two access tiers based on changing access patterns. Objects not accessed for 30 days move to a lower-cost tier, but are still immediately available with millisecond retrieval. If objects become frequently accessed again, they are moved back to the frequent access tier. There are no retrieval charges and no impact on availability or performance. This storage class is specifically designed for unpredictable access patterns and cost optimization, requiring minimal management.

AWS Documentation Extract:

" S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable. "

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

B: Glacier Deep Archive is for archival, not for low-latency millisecond access.

C: EFS is not optimized for object storage or global CDN distribution.

D: Cache-Control only affects CloudFront or browser caching, not S3 storage cost.

AWS Security Hub provides a centralized view of security findings across AWS accounts and services. It integrates natively with AWS Config conformance packs, which evaluate compliance against industry standards such as CIS and PCI-DSS.

From AWS Documentation:

“AWS Security Hub aggregates, organizes, and prioritizes security alerts and compliance status across AWS accounts. Use AWS Config conformance packs to assess compliance with security frameworks.”

(Source: AWS Security Hub User Guide – Managing Findings and Compliance)

Why C is correct:

Security Hub provides a centralized dashboard for compliance visibility.

Conformance packs in AWS Config automate compliance checks across accounts.

Fully managed, minimal maintenance, and integrates natively with AWS services.

Why others are incorrect:

A: Conformance packs are not a feature of Amazon Inspector.

B: Third-party tools on EC2 require management and add operational overhead.

D: Systems Manager is not designed for compliance aggregation.

The company needs a cloud-based storage solution for frequently accessed data with low latency, while retaining their current on-premises infrastructure for some data storage. AWS Storage Gateway ' sVolume Gateway with cached volumesis the most appropriate solution for this scenario.

AWS Storage Gateway - Volume Gateway (Cached Volumes):

Volume Gateway with cached volumesallows you to store frequently accessed data in the AWS Cloud while keeping the most recently accessed data cached locally on-premises. This ensures low-latency access to active data while providing scalability for the rest of the data in the cloud.

The cached volume option stores the primary data in Amazon S3 but caches frequently accessed data locally, ensuring fast access. This configuration is well-suited for applications that require fast access to frequently used data but can tolerate cloud-based storage for the rest.

Since the company is facing limited on-premises storage, cached volumes provide an ideal solution, as they reduce the need for additional on-premises storage infrastructure.

Why Not the Other Options?:

Option A (S3 File Gateway): S3 File Gateway provides a file-based interface (SMB/NFS) for storing data directly in S3. While it is great for file storage, the company’s need for block-level storage with iSCSI targets makes Volume Gateway a better fit.

Option C (Volume Gateway - Stored Volumes): Stored volumes keep all the data on-premises and asynchronously back up to AWS. This would not address the company ' s storage limitations since they would still need substantial on-premises storage.

Option D (Tape Gateway): Tape Gateway is designed for archiving and backup, not for frequently accessed low-latency data.

AWS References:

AWS Storage Gateway - Volume Gateway

AWS Service Catalogallows organizations to centrally manage commonly deployed IT services and offers self-service deployment capabilities to customers. By creatingService Catalog products, the consulting company can package their solutions and tools for easy reuse by customers while maintaining central control over configuration and access. This provides a standardized and automated solution with the least operational overhead for managing and deploying solutions across different customers.

Option A (CloudFormation): CloudFormation templates are useful but don ' t provide the same level of management and user-friendly self-service capabilities as Service Catalog.

Option C (Systems Manager): Systems Manager is more focused on managing infrastructure and doesn ' t offer the same self-service capabilities.

Option D (AWS Config): AWS Config is used for tracking resource configurations, not for deploying solutions.

AWS References:

AWS Service Catalog

After 180 days, the images become infrequently accessed but still must remain immediately available, which fits S3 Standard-IA better than One Zone-IA because the question requires high availability and redundancy throughout the lifecycle. AWS documents that Standard-IA is for long-lived, infrequently accessed data with millisecond access and multi-AZ resilience, while One Zone-IA stores data in a single AZ. After 360 days, the company still needs instant access, so S3 Glacier Instant Retrieval is the right archival class. AWS states that Glacier Instant Retrieval provides real-time access with lower storage cost than Standard-IA for archive data that still must be immediately retrievable. Glacier Flexible Retrieval would not meet the instant-access requirement.

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This solution is the most suitable for running cron jobs on AWS with minimal refactoring, while also supporting the possibility of running jobs in response to future events.

Container Image for Cron Jobs: By containerizing the cron jobs, you can package the environment and dependencies required to run the jobs, ensuring consistency and ease of deployment across different environments.

Amazon EventBridge Scheduler: EventBridge Scheduler allows you to create a recurring schedule that can trigger tasks (like running your cron jobs) at specific times or intervals. It provides fine-grained control over scheduling and integrates seamlessly with AWS services.

AWS Fargate: Fargate is a serverless compute engine for containers that removes the need to manage EC2 instances. It allows you to run containers without worrying about the underlying infrastructure. Fargate is ideal for running jobs that can vary in duration, like cron jobs, as it scales automatically based on the task ' s requirements.

Why Not Other Options?:

Option A (Lambda): While AWS Lambda could handle short-running cron jobs, it has limitations in terms of execution duration (maximum of 15 minutes) and might not be suitable for jobs that run up to 20 minutes.

Option B (AWS Batch on ECS): AWS Batch is more suitable for batch processing and workloads that require complex job dependencies or orchestration, which might be more than what is needed for simple cron jobs.

Option D (Step Functions with Wait State): While Step Functions provide orchestration capabilities, this approach would introduce unnecessary complexity and overhead compared to the straightforward scheduling with EventBridge and running on Fargate.

AWS References:

Amazon EventBridge Scheduler- Details on how to schedule tasks using Amazon EventBridge Scheduler.

AWS Fargate- Information on how to run containers in a serverless manner using AWS Fargate.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

Routing requests to the “appropriate microservices” implies Layer 7 (HTTP/HTTPS) routing such as host-based or path-based rules (for example, /customers to one service and /orders to another). In EKS, the most direct and cost-efficient way to provide this style of routing is to use Kubernetes Ingress with the AWS Load Balancer Controller to provision an Application Load Balancer (ALB). An ALB natively supports HTTP/HTTPS request routing features (paths, hosts, headers), which map cleanly to microservices patterns and reduce the need for additional infrastructure components.

A Network Load Balancer (NLB) (Option A) is primarily Layer 4 and is best for TCP/UDP/TLS pass-through and ultra-high performance. It does not provide the same native HTTP path-based routing behavior required to steer requests between microservices based on URL structure, so it often forces you to add another routing layer (like an in-cluster ingress gateway), which can increase cost and operational complexity.

Option C is not an appropriate request routing mechanism; Lambda is compute, not an ingress/routing layer for EKS. Option D (API Gateway) can front services, but for simple microservice routing into EKS it typically adds per-request costs and additional configuration compared with using an ALB that is purpose-built for HTTP load balancing and routing. API Gateway is a great fit for API management features (throttling, usage plans, keys, transformations), but those are not requirements here.

Therefore, B is the most cost-effective and architecturally aligned solution for HTTP routing across EKS-hosted microservices using managed AWS-native ingress integration.

AWS KMS automatic key rotationis the simplest and most operationally efficient solution. Enabling automatic key rotation ensures that KMS automatically generates new key material for the key every year without requiring manual intervention.

Option B:Configuring alerts to rotate keys introduces operational overhead as the actual rotation must still be managed manually.

Option C:Scheduling a Lambda function to rotate keys adds unnecessary complexity compared to enabling automatic key rotation.

Option D:Using a CloudFormation stack to run a Lambda function for key rotation increases operational overhead and complexity unnecessarily.

AWS Documentation References:

AWS KMS Key Rotation

Using Customer-Managed Keys with Amazon RDS

The best AWS-native design isAmazon Kinesis Data Streamsfor real-time ingestion andAmazon OpenSearch Servicefor near-real-time search and analytics. AWS describes OpenSearch Service as a managed search and analytics engine for real-time application monitoring and similar use cases, and AWS also provides architectures for delivering real-time data from Kinesis into OpenSearch. QuickSight is then appropriate for dashboards and visualizations. The other answers either introduce more operational overhead or use services that are not the best fit for real-time search workloads. For an on-premises search application moving to AWS, Kinesis plus OpenSearch is the most natural managed solution.

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The workload is Windows-based and requires a shared file system with SMB support and very high throughput for 4K video editing.

Amazon FSx for Windows File Server is a fully managed, highly available native Windows file system that supports the SMB protocol, Active Directory integration, and can deliver high throughput and low latency suitable for media workloads.

FSx for Windows File Server supports automatic storage scaling to grow file system capacity as data increases, matching the requirement of +1 TB per week with minimal admin effort.

A Multi-AZ SSD deployment provides high availability and performance.

Why others are not correct:

B: Amazon EFS is NFS, not SMB, and is optimized for Linux clients, not Windows-based environments.

C: FSx for Lustre is a high-performance POSIX file system typically used with Linux-based HPC workloads, not Windows + SMB.

D: S3 File Gateway is not designed for sustained multi-GB/s shared-edit performance and adds additional complexity; it is more suited for backup/archival and limited shared file access.

AWS Global Accelerator: This service provides a global fixed entry point to your applications and optimizes the path to your application through the AWS global network, reducing latency and improving performance.

Accelerated TCP Connections:

Global Accelerator uses the AWS global network to route traffic to the nearest edge location, improving the performance and reliability of your live streams.

It provides static IP addresses that act as a fixed entry point to your application, simplifying DNS management.

High-Quality Streams:

By leveraging Global Accelerator, reporters can send live streams with the highest quality and low latency.

This service automatically reroutes traffic to the nearest available AWS Region, ensuring consistent performance even during traffic spikes or failures.

Operational Efficiency: Using Global Accelerator simplifies the network setup and provides an optimized path for live streams without the need for complex configurations, making it an efficient solution for real-time streaming applications.

S3 Intelligent-Tiering is designed for data with unknown or changing access patterns. It automatically moves objects between frequent and infrequent access tiers as needed, with no retrieval fees for accessing data in any tier, and no performance impact. Minimal management overhead is required because AWS manages all transitions automatically. This class is cost-optimized and meets all requirements listed.

AWS Documentation Extract:

" S3 Intelligent-Tiering is the only storage class that automatically moves data between frequent and infrequent access tiers when access patterns change, with no retrieval charges and no impact on performance. It is designed to optimize costs automatically when data access patterns are unpredictable. "

(Source: Amazon S3 documentation, Intelligent-Tiering storage class)

Other options:

B: Storage class analysis only provides recommendations, not actual storage tiering.

C & D: S3 Standard-IA and S3 One Zone-IA both have retrieval fees and may impact retrieval time and data durability.

Amazon DynamoDB supports TTL (Time To Live) for automated, scheduled deletion of expired items. It also offers point-in-time recovery (PITR) to restore the table to any second within the retention window (typically up to 35 days), providing full data durability and protection. DynamoDB can efficiently handle frequent updates and offers predictable performance. MemoryDB is an in-memory store, not designed for durable recovery. S3 with lifecycle policies does not handle updates as efficiently for small, frequent writes and is not as optimal for session data.

Reference Extract:

" DynamoDB supports TTL for automated expiration and deletion of items and PITR for continuous backups and restoration of data. "

Source: AWS Certified Solutions Architect – Official Study Guide, DynamoDB section.

IAM Identity Center (formerly AWS SSO) is designed for:

Federated access from external directories (e.g., Active Directory, Okta)

Centralized permission management

Support for MFA

Granular control via Attribute-based access control (ABAC)

“IAM Identity Center allows you to manage SSO access to AWS accounts and business applications centrally. You can assign users and groups permissions based on directory attributes such as Region and job role.”

— IAM Identity Center Docs

This option ensures:

Federated, centralized access

Region-specific permissions

MFA and role mapping via existing directory service

Explanation (AWS Docs):

A: Use Direct Connect with a private VIF to ensure private connectivity from on-premises to AWS.

E: Peer the networking VPC with VPCs in the accounts hosting S3 buckets to allow private routing.

“A private virtual interface enables private connectivity to your VPC.”

— Direct Connect Private VIF

The first step is to configure a delegated administrator account for IAM Access Analyzer at the organization level. Only after delegating the administrator account can you aggregate Access Analyzer findings from all member accounts into a designated audit account. This must be set up in the AWS Organizations management account.

AWS Documentation Extract:

“You must designate a delegated administrator for IAM Access Analyzer at the organization level. The delegated administrator account aggregates findings from all member accounts.”

(Source: IAM Access Analyzer documentation)

A, C, D: These steps do not establish the organization-wide aggregation required for Access Analyzer.

To allocate costs based on team ownership, AWS recommends tagging resources using cost allocation tags. Activating the “owner” tag as a cost allocation tag allows AWS Cost Explorer to categorize and group spending. Additionally, filtering for Amazon ECS services enables the visibility into service-specific usage. This approach is both scalable and operationally efficient.

Amazon Cognitoprovides scalable, serverless authentication, andLambda@Edgeis used for authorization, providing low-latency access control at the edge.Amazon CloudFrontserves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.

Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.

Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.

Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.

AWS References:

Amazon Cognito

Lambda@Edge

To grant the ECS application the least privilege, you create an IAM policy that explicitly lists the ARNs of the specific S3 buckets the task should access. You then attach this policy to the task role (not the instance role), ensuring only that task has the necessary permissions. This approach avoids granting permissions to other EC2 instances or services and restricts access strictly to the required buckets.

AWS Documentation Extract:

" Attach an IAM policy granting access to specific resources to the ECS task role, not to the instance role, to ensure that only the container has access according to the principle of least privilege. "

" Use explicit ARNs to control access only to specified S3 buckets. "

(Source: Amazon ECS documentation, IAM Roles for Tasks)

A: Tag-based access control for S3 buckets is possible but less direct and commonly used for identity-based access.

C: Attaching the policy to the instance role could grant unintended permissions to all containers or services running on the instance.

D: Wildcard ARNs grant broader access than necessary.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

To capture traffic metadata to and from network interfaces in a VPC, the correct telemetry source is VPC Flow Logs. Flow Logs can publish records to destinations such as CloudWatch Logs, which is a natural first landing zone for near-real-time log ingestion and centralized retention/permissions. The next requirement is to stream that data to Amazon OpenSearch Service for analysis with minimal operational overhead.

Amazon Data Firehose is purpose-built for fully managed delivery of streaming data to destinations including OpenSearch. Firehose handles buffering, batching, retry behavior, and optional transformations, and it minimizes the need to build and operate custom consumers. Therefore, sending VPC Flow Logs to CloudWatch Logs and then using Firehose to deliver to OpenSearch is an operationally efficient and scalable pattern.

Option A uses Kinesis Data Streams, which would require building/operating consumers (or additional integration components) to read from the stream and load into OpenSearch, adding operational complexity compared with Firehose’s managed delivery. Options C and D are incorrect because CloudTrail records API activity and governance events, not VPC network flow records; VPC Flow Logs do not publish to CloudTrail trails.

Thus, B best meets the requirement: Flow Logs capture the network interface traffic metadata; CloudWatch Logs provides near-real-time log collection; and Firehose provides managed streaming delivery into OpenSearch for indexing and analytics with minimal operational effort.

Amazon CloudFront caches eligible GET responses at globally distributed edge locations. After the first request is served from the API Gateway origin, subsequent requests can be returned from a nearby edge cache without invoking API Gateway’s Lambda integration. A one-hour TTL aligns with the catalog’s update frequency and substantially reduces both global network latency and Lambda invocation volume. API Gateway caching also reduces backend calls, but its cache is associated with the API stage in the deployment Region and does not provide the same globally distributed edge-cache benefit. Increasing Lambda memory might improve individual execution performance but would not eliminate repeated executions for identical catalog requests. Replacing the serverless API with an Application Load Balancer and EC2-based cache would increase operational overhead. CloudFront therefore satisfies both the global-latency and backend-load requirements.

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A target tracking policy automatically adds or removes EC2 capacity to keep the selected metric close to a defined target. Setting ASGAverageCPUUtilization to 75% causes the Auto Scaling group to scale out before CPU utilization reaches the observed 90% instability threshold. A target of 90% would allow the application to reach the known failure condition before sufficient scaling occurred. Manually increasing minimum capacity only after instability appears is reactive and creates unnecessary administrative work. Scheduled scaling is unsuitable because the question describes occasional utilization increases rather than a predictable daily traffic pattern. Target tracking continuously evaluates demand and manages capacity without requiring operators to determine exact scaling increments or schedules. Therefore, the 75% CPU target provides the required stability with the least ongoing operational overhead.

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Amazon Route 53 is a highly available and scalable authoritative DNS service. To move a public domain, you create a public hosted zone, import or recreate the zone records, and then update the registrar to use the Route 53 name servers assigned to the zone. Route 53 is globally distributed and designed for rapid propagation and resilience, and supports features such as health checks and routing policies. A private hosted zone (Option B) is resolvable only by VPCs that are associated with it and is not for public internet DNS. Directory Service and zone transfers (Option C) are unrelated to Route 53 public DNS hosting. Route 53 Resolver inbound endpoints (Option D) provide hybrid DNS forwarding for VPC workloads, not authoritative public hosting. Therefore, the fastest AWS-native migration path to a resilient managed DNS is to create a Route 53 public hosted zone and import the existing zone file.

CloudTrail log file validation ensures that the log files have not been altered or deleted after delivery, providing an immutable audit log. Using KMS-managed encryption keys for CloudTrail log files adds another layer of data security, and AWS Config can monitor compliance to ensure this security is always enforced.

Reference Extract:

" CloudTrail log file validation provides assurance about the integrity of CloudTrail logs. Using SSE-KMS encryption and monitoring with AWS Config helps secure and audit logs for compliance. "

Source: AWS Certified Solutions Architect – Official Study Guide, CloudTrail Security section.

Amazon RDS Proxy is designed to manage a large number of database connections from applications, especially serverless applications that can scale quickly. It improves application availability and scalability by pooling and sharing established database connections. This reduces the overhead of database connections and prevents overload during traffic spikes.

Workload Discovery on AWS automatically scans AWS accounts and Regions, identifies deployed resources, and generates relationship-aware architecture diagrams. This provides a complete workload map with minimal operational effort.

Systems Manager Inventory (Option A) collects instance metadata but does not build multi-account architecture diagrams.

Step Functions (Option B) and X-Ray (Option D) require manual diagramming and do not meet the operational-efficiency requirement.

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Aurora blue/green deployments are specifically designed for:

Safely testing schema changes and database upgrades in an isolated environment (green) without impacting production (blue).

Performing a fast, low-downtime switchover once testing is complete and validated.

In this pattern:

The blue environment is the current production database.

The green environment is a synchronized copy used for testing changes.

You apply schema changes to the green environment, run tests, and when ready, perform a managed switchover that minimizes downtime and risk.

Why others are not ideal:

A: A separate staging cluster allows testing but does not provide automated, low-downtime synchronization and switchover.

B: Aurora read replicas are for read scaling; schema changes on replicas are not supported in the way described, and promotion alone doesn’t solve controlled testing and replication of changes.

D: Moving to DynamoDB changes database engines and data models entirely, and does not match the requirement to keep using Aurora MySQL.

Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.

AWS Documentation Extract:

“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”

(Source: Apache Flink on AWS documentation)

B: S3/Redshift is not real-time and adds complexity.

C, D: EC2/ECS solutions are not serverless or fully managed.

AWS DataSync provides managed, incremental, parallelized transfers between EFS file systems across Regions, supporting scheduled or continuously running tasks with automatic change detection, encryption in transit, and integrity verification. You can configure two tasks in opposite directions (bi-directional) to achieve two-way synchronization with high availability using agents in each Region and EFS locations connected via VPC endpoints. Native EFS replication (B) is one-way (read-only target) and not intended for active/active two-way sync. Options A and D rely on custom rsync and cross-Region mounts or SFTP, which introduce operational overhead, latency, and fail to provide resilient, managed synchronization. DataSync minimizes operational burden while delivering fault-tolerant, scalable, cross-Region EFS sync.

Amazon EC2 Auto Scaling publishes group-level metrics, including the number of in-service instances. A CloudWatch alarm can evaluate whether that count remains above 180 for a continuous 60-minute period. A step scaling action can then reduce desired capacity, allowing the group to support short-lived surges up to 300 instances while limiting the cost of sustained excess capacity. Scheduled scaling cannot handle unpredictable surges, and maximum instance lifetime replaces individual instances rather than controlling total group capacity. Directly terminating instances through an alarm would bypass normal Auto Scaling capacity management. The original option’s wording is technically imperfect because step scaling adjusts desired capacity; it does not directly rewrite the group’s maximum-size property. Option D is correct when interpreted as a scale-in action that reduces desired capacity to approximately 180 instances.

================

Amazon S3 Access Points simplify managing data access for shared datasets in S3 by allowing the creation of distinct access policies for different applications or users. Each access point has its own policy and can be managed independently. This method avoids overloading a single bucket policy and helps remain within policy size limits.

Option D provides a scalable and operationally efficient solution by offloading individual access controls from a central bucket policy to individually managed access points, which is ideal for environments with many consuming applications.

AWS WAF has limits on how much of an HTTP request body it can inspect. When requests exceed the inspectable size, AWS WAF treats the body as oversize relative to the configured inspection limits, which can lead to rules not evaluating the entire body content. If suspicious payloads are embedded beyond the inspected portion of a large POST request (for example, > 8 MB), WAF cannot reliably detect them purely through body inspection rules.

Given this constraint, the most effective way to “ensure” proper protection is to implement an oversize handling strategy using AWS WAF rule logic: block oversized requests by default and then explicitly allow oversized requests only from known legitimate sources. Option A accomplishes this by adding a rule that blocks oversize requests (so attackers cannot bypass inspection by sending very large bodies) and a higher-priority allow rule to permit large requests from trusted hosts (for example, specific known partners, internal CIDRs, or authenticated upstream systems). This design reduces the attack surface and provides deterministic behavior for requests that cannot be fully inspected.

Option B is incorrect because Shield Advanced is for DDoS protection and does not extend WAF’s request-body inspection size. Option C is unrelated: Content-Type can influence application parsing, but it will not overcome WAF body-size inspection limitations. Option D is not a practical fit for AWS WAF inspection because WAF evaluates requests at the edge/service layer; it does not natively “call Lambda to rewrite the request body” before WAF evaluates it. Any preprocessing would require a different architectural pattern (such as handling uploads out-of-band), which is beyond the scope and would add operational complexity.

Therefore, A is the correct approach: implement explicit oversized request handling by blocking by default and allowing only vetted large requests.

To securely access AWS services like S3 and Secrets Manager from a private VPC without using public IPs, interface VPC endpoints are required. These endpoints are accessible via private IP addresses. For the application to reach these endpoints, appropriate routes must be configured in the route table.

For a MySQL database experiencing high write operations, using Amazon RDS with Provisioned IOPS (io1 or io2) SSD storage is recommended to achieve consistent and low-latency performance. Provisioned IOPS allows you to specify a desired IOPS rate, which is crucial for write-intensive workloads.

Monitoring write operation metrics through Amazon CloudWatch enables you to observe performance and adjust the provisioned IOPS as needed to meet application demands.

To enable DNS resolution from AWS VPCs to on-premises DNS servers over Direct Connect or VPN, AWS recommends using Amazon Route 53 Resolver with outbound endpoints. An outbound endpoint allows DNS queries originating in the VPC to be forwarded to a customer-managed DNS server (e.g., on-prem).

Next, a forwarding rule (Forward type) must be created to forward DNS queries for the custom domain internal.example.com to the on-premises DNS IP addresses. This rule defines what domain names are forwarded and to which DNS servers.

Finally, the rule must be associated with all workload VPCs to allow those VPCs to use the rule. There is no need to deploy endpoints in every VPC — one outbound endpoint is sufficient and can be shared across VPCs via rule association.

???? Reference:

Route 53 Resolver Endpoints

Best practices for hybrid DNS resolution

For bidirectional communication such as chat applications, Amazon API Gateway WebSocket API is the correct service. WebSocket APIs allow clients to establish long-lived connections and exchange messages with the backend Lambda functions in real time.

HTTP APIs and REST APIs are suitable for request-response models, not continuous two-way communication. CloudFront cannot maintain stateful WebSocket connections, so only Option C fits the requirements for a real-time, bidirectional application.

AWS Fargate with Spot capacity is the most cost-effective and serverless container option for short-duration jobs that are flexible on start time. Since the job is not latency-critical and runs nightly, it is a good candidate for Fargate Spot, which can offer up to 70% cost savings over On-Demand pricing.

The job runs for 1 hour, which exceeds the AWS Lambda maximum execution time (15 minutes). Therefore, Lambda is not suitable. EC2-based solutions involve higher operational overhead and cost, making Fargate Spot the best low-cost, low-maintenance solution.

EBS gp3 volumes allow you to independently configure IOPS and throughput, up to 16,000 IOPS, regardless of the volume size, and at a lower cost compared to io1/io2 provisioned IOPS volumes. This meets the requirement for cost-effective, predictable IOPS performance for Amazon RDS for PostgreSQL.

AWS Documentation Extract:

" With gp3 volumes, you can provision performance independent of storage capacity, up to 16,000 IOPS. This allows for cost-effective scaling for applications that require high performance at a lower price point compared to io1. "

(Source: Amazon EBS documentation, gp3 Volumes)

A: gp2 ties IOPS to volume size; 5 TiB is wasteful if only 15,000 IOPS are needed.

B: io1 works but is significantly more expensive than gp3 for most workloads.

D: Magnetic volumes do not support high IOPS.

For logs that are:

Written and processed within a short period (24 hours)

Accessed quickly for compute/analytics

With unknown object count and size

Amazon S3 Standard is the most appropriate and cost-effective. Intelligent-Tiering is designed for data stored for longer periods (typically 30+ days) with changing access patterns and charges a per-object monitoring and automation fee that becomes inefficient for very short-lived objects.

S3 Glacier Deep Archive and S3 One Zone-IA are optimized for long-term archival or infrequently accessed data with retrieval time or availability constraints that are not suitable for nightly active processing.

The most cost-effective way to provide consistent SQL query performance on a heavily structured dataset, where some data is accessed more frequently, is to use Amazon Redshift as your main data warehouse for hot data and Amazon Redshift Spectrum to query cold data that remains in S3. With this approach, frequently queried data is loaded into Redshift for fast, consistent querying, while infrequently accessed data is left in S3 and accessed on demand via Spectrum, avoiding unnecessary data warehousing costs. Amazon Kinesis Data Firehose provides an easy and scalable way to ingest streaming data directly to both S3 and Redshift.

AWS Documentation Extract:

" Amazon Redshift Spectrum allows you to run queries against exabytes of data in Amazon S3 without loading or transforming the data. Load hot data into Redshift for fast access and query cold data in S3 with Spectrum, optimizing both cost and performance. "

(Source: Amazon Redshift documentation, Spectrum)

A: Athena is good for ad hoc queries but not for consistent, high-performance SQL workloads.

B, C: Loading all data into Redshift is not cost-effective if some data is infrequently accessed.

CloudFront Signed URLs: These URLs allow you to provide limited access to content that is being served through an Amazon CloudFront distribution. Signed URLs can be generated to grant time-limited access to premium customers.

Content Restriction:

By using CloudFront signed URLs, you can control access to your media streams and file content stored in S3.

These URLs can be customized with an expiration time, ensuring that access is only available for a specific period, which is useful for scenarios like movie rentals or music downloads.

Security and Flexibility:

Signed URLs ensure that only authenticated users (premium customers) can access the restricted content.

This approach integrates seamlessly with CloudFront and S3, providing an efficient way to manage access controls without additional overhead.

Operational Efficiency: Using CloudFront signed URLs leverages AWS managed services to handle the complexity of access control, reducing the need for custom implementation and maintenance.

To secure data in transit, the company should use AWS Certificate Manager (ACM) to provide SSL/TLS certificates for the Application Load Balancer. ACM allows easy provisioning, management, and renewal of public and private certificates, ensuring secure communication between users and applications.

To secure data at rest, AWS Key Management Service (KMS) is used to manage encryption keys for Amazon EBS volumes. EBS integrates with AWS KMS, allowing for server-side encryption using KMS-managed keys (SSE-KMS), thus meeting the encryption at rest requirement.

Other options:

GuardDuty (A) is for threat detection, not encryption.

AWS Shield (B) protects against DDoS attacks, not encryption.

Secrets Manager (D) manages credentials, not general data encryption.

This solution follows the AWS Well-Architected Framework – Security Pillar.

???? References:

Encrypting EBS volumes with KMS

Using ACM with ALB

AWS KMS with SSE-KMS provides granular key management and auditability. All use of KMS keys is logged in AWS CloudTrail, which allows compliance teams to monitor encryption and decryption operations. A bucket policy can be configured to enforce uploads only with the designated KMS key, ensuring that users cannot bypass encryption or change methods. Option A (SSE-S3 with bucket policy) enforces encryption but does not provide the same level of control or auditable key usage. Option C (client-side encryption) increases complexity and key management burden. Option D prevents bucket setting changes but does not prevent unencrypted uploads. Therefore, B ensures the most granular control, auditability, and compliance with financial data requirements.

A. On-premises tool via internet:Incurs high costs due to large data transfers over the internet.

B. AWS Region tool via internet:Does not utilize Direct Connect, leading to potential latency and higher costs.

C. On-premises tool via Direct Connect:Adds latency for querying and visualization.

D. AWS Region tool via Direct Connect:Reduces latency and leverages Direct Connect for optimized data transfer costs.

This solution offers the least operational overhead while meeting the security and global availability requirements:

Amazon CloudFront and S3: Hosting the static frontend on S3 and serving it via CloudFront provides low-latency global distribution, high availability, and security. S3 is a cost-effective and serverless option for hosting static assets, and CloudFront ensures that the application is cached closer to the users, reducing latency globally.

AWS Lambda and API Gateway: Refactoring the microservices to use Lambda functions with API Gateway allows for a fully serverless, scalable, and highly available backend. This reduces the need for managing EC2 instances, as Lambda automatically scales to meet demand and only charges for the actual usage.

RDS for MySQL: Migrating the MySQL database from an EC2 instance to Amazon RDS significantly reduces operational overhead. RDS manages backups, patching, and scaling, and it offers high availability options (e.g., Multi-AZ).

Option AandDinvolve using EC2 Reserved Instances for the database, which requires more operational maintenance than using RDS.

Option Csuggests using a Network Load Balancer with Lambda, which adds unnecessary complexity for this use case.

AWS References:

Amazon S3 and CloudFront Integration

AWS Lambda with API Gateway

Amazon RDS for MySQL

Amazon EFS is a regional, elastic file system that “scales to petabytes” and can be accessed from “thousands of compute instances” concurrently. You can mount EFS across VPCs and across AWS accounts by providing network connectivity (e.g., VPC peering) to the EFS mount targets and allowing NFS (TCP 2049) in the mount target security group, optionally using EFS access points and a file system policy for cross-account access control. VPC peering has no hourly charge and minimal operational overhead, and EFS automatically scales as files are added—meeting the scalability and cost goals.

Option A duplicates data, incurs DataSync and extra storage costs, and adds sync lag.

Option C adds an extra Lambda hop and complexity without exposing a shared filesystem to the primary function.

Option D is impractical: Lambda layers are immutable artifacts with tight size limits (up to 50 MB compressed/250 MB uncompressed) and are not suited for dynamic, growing file sets.

Latency-based routing in Amazon Route 53 is designed to route users to the Region that provides the lowest network latency, based on Amazon’s measurements of latency between AWS Regions and users’ networks. For applications deployed in multiple Regions, this provides the highest performance experience for global users.

Therefore, creating an A record with a latency routing policy is the correct choice.

Geolocation (Option B) routes based on user location, which may not always correspond to the lowest latency.

Failover (Option C) is for active-passive architectures, not performance optimization.

Geoproximity (Option D) is more complex and focused on directing traffic based on geographic bias rather than measured latency.

CloudFront requires ACM certificates to be created in us-east-1. Origin Access Control (OAC) securely allows CloudFront to upload objects to S3 without exposing the bucket publicly. Transfer Acceleration and S3 website endpoints are unnecessary or incompatible for this use case.

AWSDirect Connectis the best solution for establishing a consistent, low-latency connection from an on-premises data center to AWS without using the public internet. Direct Connect offers dedicated, high-throughput, and low-latency network connections, which are ideal for performance-sensitive applications like a trading platform that processes high volumes of market data and stock trades.

Direct Connect provides a private connection to your AWS VPC, ensuring that data doesn ' t traverse the public internet, which enhances both security and performance consistency.

AWS References:

AWS Direct Connectprovides a dedicated network connection to AWS services with consistent, low-latency performance.

Best Practices for High Performance on AWSfor performance-sensitive workloads like trading platforms.

Why the other options are incorrect:

A. AWS Client VPN: While this offers secure connectivity, it ' s over the public internet and is not designed for the low-latency, high-performance needs of a trading platform.

C. AWS PrivateLink: PrivateLink is used for connecting VPCs and services within AWS, but it is not designed for connecting on-premises data centers to AWS.

D. AWS Site-to-Site VPN: Although this provides secure connectivity, it uses the public internet, which can introduce latency and doesn ' t meet the low-latency requirements of the use case.

Amazon RDS for MySQL Reserved Instances provide significant savings over on-demand pricing when you plan to run the database for long periods. This is the most cost-effective option for non-production, long-running managed MySQL workloads.

Reference Extract:

" Reserved Instances provide a significant discount compared to On-Demand pricing and are recommended for steady-state workloads that run for an extended period. "

Source: AWS Certified Solutions Architect – Official Study Guide, RDS Cost Optimization section.

The correct answer isAbecause the application must acceptTLS connections from IPv4 clients, provideoutbound internet access, present asingle entry point, and maintain thelowest possible attack surface. Placing the Amazon ECS tasks inprivate subnetsis the key security design decision because it prevents direct inbound access from the internet to the tasks themselves. The public-facing entry point is the load balancer, while outbound internet access for the private tasks is provided through aNAT gatewayin the public subnet.

ANetwork Load Balancer (NLB)is well suited for handlingTLSat Layer 4 and can expose a single public endpoint for client connections. Withawsvpcnetwork mode, each ECS task receives its own elastic network interface, making it straightforward to place tasks securely in private subnets while still registering them as targets behind the load balancer.

Option B is incorrect because anegress-only internet gatewayis forIPv6 outbound traffic only, while the requirement specifically mentionsIPv4 clients. Option C is incorrect because placing ECS tasks inpublic subnetsincreases the attack surface by exposing application infrastructure more directly to the internet. Option D is also incorrect for two reasons: it uses anegress-only internet gateway, which does not satisfy IPv4 outbound needs, and it places tasks inpublic subnets, which violates the goal of minimizing exposure.

AWS security design guidance emphasizes reducing exposure by placing application workloads in private subnets and exposing only the required front-end endpoint. Therefore, a publicNLBwith private ECS tasks and aNAT gatewayis the most secure and appropriate architecture.

Securing the root user account requires setting a strong password and enabling multi-factor authentication (MFA). AWS recommends never sharing the root user credentials, setting up individual IAM users for everyday operations, and always protecting the root user with MFA for maximum security.

Reference Extract:

" AWS recommends securing the root user with a strong password and enabling multi-factor authentication (MFA). Do not use or share root credentials for everyday tasks. "

Source: AWS Certified Solutions Architect – Official Study Guide, IAM and Security Best Practices section.

To optimize costs for long-term data storage, AWS recommends using S3 Lifecycle policies to automate transitions across storage classes and ultimately expire old data.

S3 Standard-IA is suited for data that is accessed less frequently but requires millisecond retrieval times. It is ideal for 6-month-old data still used in monthly reports.

S3 Glacier Deep Archive is the lowest-cost option, designed for data accessed once or twice a year, such as regulatory audits — perfect for 2+ year-old data.

Lifecycle expiration rules allow S3 to automatically delete objects older than 7 years, aligning with the business retention policy.

These transitions are cost-effective and fully automated, aligning with the Cost Optimization pillar in the AWS Well-Architected Framework.

???? Reference:

S3 Storage Class Comparison

Lifecycle Management

The critical requirement is immediate processing upon customer submission, with a typical compute duration of about 10 seconds. This is an ideal fit for a synchronous, request-driven, serverless pattern: API Gateway + AWS Lambda. API Gateway provides a managed HTTPS front door for the application, handles request validation/throttling if needed, and integrates directly with Lambda. Lambda is designed for short-lived computations, scales automatically with request volume, and can complete 10-second tasks well within typical Lambda execution limits.

Option A therefore meets the requirement with low latency and minimal operational overhead. When a customer submits data, API Gateway invokes the Lambda function immediately, and the function returns the calculated premium response. This preserves an interactive user experience and avoids provisioning or managing servers. It also handles bursts gracefully because Lambda concurrency can scale quickly, and API Gateway can absorb high request rates.

Option B is unsuitable because launching a Spot instance per upload introduces significant startup latency (instance launch time) and is not reliable due to Spot interruptions. Option C and D are not “immediate”: Transfer Family is for file transfers, and scheduled EKS jobs or Batch jobs introduce queuing and scheduling delays. While Batch can be event-driven, it is typically used for longer-running or batch-oriented workloads and still has job-start overhead compared to Lambda.

Therefore, A best matches the requirements for immediate execution, scalability, and low operational burden while maintaining responsive performance for customers.

This solution is designed to provide high availability and low-latency access to block storage across multiple Availability Zones with minimal implementation effort.

Windows Server Cluster Across AZs: Configuring a Windows Server Failover Cluster (WSFC) that spans two Availability Zones ensures that the application can failover from one instance to another in case of a failure, meeting the high availability requirement.

Amazon FSx for Windows File Server: FSx for Windows File Server provides fully managed Windows file storage that is accessible via the SMB protocol, which is suitable for Windows-based applications. It offers high availability and can be used as shared storage between the cluster nodes, ensuring that both nodes have access to the same data with low latency.

Why Not Other Options?:

Option B (EBS with application-level replication): This requires complex configuration and management, as EBS volumes cannot be directly shared across AZs. Application-level replication is more complex and prone to errors.

Option C (FSx for NetApp ONTAP with iSCSI): While this is a viable option, it introduces additional complexity with iSCSI and requires more specialized knowledge for setup and management.

Option D (EBS with EBS-level replication): EBS-level replication is not natively supported across AZs, and setting up a custom replication solution would increase the implementation effort.

AWS References:

Amazon FSx for Windows File Server- Overview and benefits of using FSx for Windows File Server.

Windows Server Failover Clustering on AWS- Guide on setting up a Windows Server cluster on AWS.

Deploying the web application on EC2 instances in private subnets behind an internal ALB ensures that the application is not directly accessible from the internet. By setting up a Site-to-Site VPN connection, the application can be accessed securely from the company ' s office network. Deploying NAT gateways in public subnets allows instances in private subnets to initiate outbound connections to the internet for downloading security patches.

AWS recommends using anIAM role attached to the EC2 instanceso the application can obtaintemporary credentialsautomatically instead of storing long-term access keys in code or configuration. That directly follows AWS security guidance for workloads running on EC2. The role should grant only the specific S3 permissions the application needs, which satisfies the principle of least privilege. Administrative access is too broad, and IAM users with access keys are less secure because they require credential distribution and rotation. The best-practice design is therefore to attach an IAM role with only the required S3 actions to the instance profile and let the SDK retrieve temporary credentials from the instance metadata service.

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Amazon EMR allows the creation of security configurations to specify settings for encrypting data at rest, data in transit, or both. These configurations can be applied to clusters to ensure that data stored in Amazon S3, local disks, and data moving between nodes is encrypted.

By creating and applying an EMR security configuration, the company can ensure that all data processing complies with encryption requirements for sensitive patient data.

HPC workloads require high-throughput, low-latency networking, especially for tightly-coupled applications like weather modeling, genomics, or real-time rendering.

A cluster placement group places instances in the same Availability Zone and on physically connected hardware, reducing network latency and increasing throughput.

Elastic Fabric Adapter (EFA) is a network device for EC2 instances that enables low-latency, high-throughput networking using OS-bypass technology, ideal for tightly-coupled HPC applications.

Each instance can support single-flow 10 Gbps bandwidth using EFA, and collectively, the cluster can achieve up to 100 Gbps aggregate throughput when properly configured.

This solution supports the Performance Efficiency and Resilience design principles and is a standard AWS-recommended pattern for HPC.

???? References:

EC2 Placement Groups

Elastic Fabric Adapter Overview

Best Practices for HPC on AWS

Amazon Route 53 Resolver endpoints allow you to integrate DNS between AWS and on-premises environments easily. By creating inbound and outbound resolver endpoints, you can configure conditional forwarding rules so that DNS queries for your on-premises AD domain are forwarded to the on-premises DNS servers. This approach is fully managed, scales automatically, and requires the least administrative overhead.

AWS Documentation Extract:

" Route 53 Resolver provides DNS resolution between AWS and on-premises environments, using endpoints and forwarding rules to manage DNS query routing seamlessly. "

(Source: Route 53 Resolver documentation)

A, D: Require provisioning, managing, and patching EC2 servers or domain controllers.

B: NS records in a private hosted zone do not provide true DNS forwarding.

AWS Lambda supports encrypting environment variables at rest using AWS KMS. You can use encryption helpers (or Lambda’s built-in support) to encrypt sensitive environment variable values using a KMS key. These encrypted variables are not visible in plaintext to developers, either in the console or when running the code.

AWS Documentation Extract:

" AWS Lambda automatically encrypts environment variables at rest. For additional security, you can use AWS KMS keys and encryption helpers to encrypt environment variables, ensuring they are never exposed in plaintext. "

(Source: AWS Lambda documentation, Environment Variables Security)

A: Does not address the issue (and adds more management overhead).

B, C: There is no native support for environment variable encryption via CloudHSM or ACM.

AWS Lambda supports functions up to 10 GB of memory and 15 minutes execution time. Each invocation here requires only 1 GB of memory and finishes in 30 seconds, making it an ideal fit for Lambda. Lambda is cost-effective for event-driven, short-duration workloads and requires no infrastructure management. AWS Glue and EMR are better suited for large-scale ETL or distributed processing, which is unnecessary and more costly for this workload.

AWS Documentation Extract:

“AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume. Lambda supports up to 10 GB memory and 15 minutes per invocation.”

(Source: AWS Lambda documentation)

B, D: AWS Glue is intended for ETL on larger datasets or batch jobs, usually with higher operational overhead and cost.

C: EMR is for large-scale distributed processing and is not cost-effective for single, fast, memory-bound jobs.

Amazon Timestream: Purpose-built time series database optimized for telemetry and IoT data ingestion and analytics.

Amazon SageMaker: Provides ML capabilities for predictive maintenance workflows.

Amazon QuickSight: Efficiently generates interactive, real-time visual reports from Timestream data.

Optimized for Scale: Timestream efficiently handles large-scale telemetry data with time-series indexing and queries.

Amazon Timestream Documentation

Amazon S3 Object Lambda allows you to add your own code to process data retrieved from S3 before it is returned to an application. You can use this to dynamically redact or remove PII for specific applications on-the-fly, eliminating the need to manage multiple buckets or datasets, thus minimizing operational overhead.

Reference Extract:

" S3 Object Lambda enables you to process and transform data as it is retrieved from S3, supporting use cases such as redacting sensitive information before returning data to an application. "

Source: AWS Certified Solutions Architect – Official Study Guide, Data Security and Transformation section.

AWS Global Accelerator provides static anycast IP addresses that remain constant regardless of Regional failover. AWS directs traffic to the optimal healthy Region without relying on DNS TTL values, eliminating the risk of stale DNS caches.

Route 53 routing (Options B and D) still depends on DNS caching behavior. CloudFront (Option A) is for content delivery, not Regional failover for applications.

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The performance issue occurs because reporting queries compete with production traffic on the same primary database instance. The best-practice AWS solution is to offload read-heavy workloads to a separate database endpoint.

Option C adds an Amazon RDS read replica, which asynchronously replicates data from the primary instance. By redirecting reporting queries to the replica endpoint, the primary database can focus on transactional workloads, significantly improving application performance and customer experience.

Read replicas are specifically designed for this use case: scaling read capacity and isolating reporting or analytics queries. This solution requires minimal changes to the reporting job (endpoint update only) and avoids overprovisioning the primary database.

Option A (RDS Proxy) improves connection management but does not reduce query load or isolate reporting traffic. Option B is invalid because Multi-AZ does not scale reads and is not configurable across three AZs for a single instance. Option D increases instance size but does not address the underlying contention between workloads and increases cost unnecessarily.

Therefore, C is the most efficient and scalable solution to minimize reporting impact while maintaining high performance and availability.

Throughput Optimized HDD (st1) is the correct and most cost-effective EBS choice because the workload reads and processes large log files sequentially. AWS defines st1 volumes as low-cost magnetic storage designed for frequently accessed, throughput-intensive workloads, including log processing. This workload does not require high random IOPS or ultra-low latency, so gp3 and io1 would be more expensive than necessary. Cold HDD (sc1) is cheaper but intended for less frequently accessed data, so it is not appropriate for an active log-processing application. Because the company wants minimal architectural change from SAN-based block storage, an EBS volume preserves the block-storage model while meeting the sequential throughput requirement.

Amazon EFS is a “fully managed, elastic, shared file system” that provides NFSv4 access across many EC2 instances and AZs, ideal for lift-and-shift of Linux NFS workloads. AWS DataSync “automates moving data between on-premises NFS servers and Amazon EFS,” performing incremental, parallel transfers with encryption and integrity checks, enabling cutover with minimal disruption. FSx for Lustre (A) targets HPC and S3-backed workloads, not general NFS shares. FSx for Windows (D) exposes SMB, not NFS. S3 (C) is object storage and not directly NFS-accessible by multiple EC2 hosts. EFS + DataSync satisfies NFS compatibility, multi-client access, and low-touch migration while remaining cost-effective for a 200-GB dataset.

TooManyRequestsException errors occur when Lambda exceeds concurrency limits. The recommended pattern is to use Amazon SQS with Lambda to decouple and buffer traffic, ensuring that bursts of requests are queued and processed smoothly. Enabling provisioned concurrency for Lambda ensures that functions are pre-initialized and ready to handle spikes in load with low latency. Step Functions (B) is designed for workflow orchestration, not high-throughput request buffering. SNS with Lambda (C) does not provide buffering and may overwhelm Lambda during bursts. Reserved concurrency (D) limits function scaling instead of improving resilience. Therefore, option A provides a scalable and resilient solution, minimizing errors during traffic surges.

For predictable workloads requiring a relational database, Amazon RDS for MySQL offers a managed, cost-effective solution. Storing product images in Amazon S3 is highly durable and cost-efficient, especially for static assets like images. This combination leverages managed services to reduce operational overhead and costs.

This is a classic fanout pattern. AWS documentation states that Amazon SNS can fan out messages to multiple subscribers, including Amazon SQS queues. AWS also notes that using SQS with SNS gives each subscriber its own durable queue, which buffers events independently and allows consumers to process at their own pace. That directly satisfies the need for every consumer to receive each order event, to absorb bursts, and to let new consumers be added without changing the producer. A single SQS queue would distribute messages across consumers instead of delivering every event to every consumer. Direct EventBridge targets do not provide the same durable per-consumer buffering described in the question. SNS plus one SQS queue per consumer is the most flexible and resilient design.

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Cluster Placement Group: This type of placement group is designed to provide low-latency network performance and high throughput by grouping instances within a single Availability Zone. It is ideal for applications that require tightly coupled node-to-node communication.

Configuration:

When launching EC2 instances, specify the option to launch them in a cluster placement group.

This ensures that the instances are physically located close to each other, reducing latency and increasing network throughput.

Benefits:

Low-Latency Communication: Instances in a cluster placement group benefit from enhanced networking capabilities, enabling low-latency communication.

High Network Throughput: The network performance within a cluster placement group is optimized for high throughput, which is essential for HPC workloads.

Using API Gateway and Lambda enables serverless handling of form submissions with minimal cost and infrastructure. When coupled with Amazon SNS, it allows instant email notifications without running servers, making it ideal for low-traffic workloads.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):

The requirement is an automated notification 30 days before an imported ACM certificate expires, delivered to the security team via an existing SNS topic with email subscription. The most operationally efficient approach is to use Amazon EventBridge with the managed event type for ACM certificate expiration. ACM publishes an event when a certificate is approaching expiration, and EventBridge can match that event and route it directly to a target service without custom polling logic.

Option D uses an EventBridge rule for the ACM Certificate Approaching Expiration event and sets the SNS topic as the target. This directly delivers an alert to the existing notification channel (email via SNS) and requires minimal code and minimal ongoing maintenance. It also avoids building and scheduling a scanner that must enumerate certificates, calculate dates, handle pagination, and manage failures.

Option C sends the event to SQS. While SQS is useful for decoupling and buffering, the requirement is to notify the security team, and SNS is already configured for email delivery. Using SQS would add an extra consumer component to read from the queue and publish notifications, which is additional operational overhead.

Options A and B require a custom Lambda-based scanning solution. That introduces scheduling (for example, EventBridge schedule), logic to detect “30 days remaining,” error handling, and ongoing maintenance. Since ACM already emits a purpose-built event for expiring certificates, polling is unnecessary and less efficient.

Therefore, D is the best solution: it uses a native event from ACM, routes it through EventBridge, and notifies the security team through the existing SNS topic with the least operational effort.

Why Option B is Correct:

AWS WAF: Provides a simple way to create geographic match rules to block or allow traffic based on country IP ranges.

Least Operational Overhead: Attaching the WAF rule to the ALB ensures centralized control without modifying ACLs or instance firewalls.

Why Other Options Are Not Ideal:

Option A: Network ACLs operate at the subnet level and can become complex to manage for dynamic or evolving IP ranges.

Option C: Managing IP-based rules in security groups and ACLs lacks scalability and does not provide country-based filtering.

Option D: Configuring host-based firewalls increases operational overhead and does not leverage AWS-managed solutions.

AWS References:

AWS WAF Geomatch:AWS Documentation - WAF Geomatch

Amazon EKS Anywhere is the best fit because it is specifically designed to run Kubernetes on customer-managed infrastructure in on-premises and edge environments while simplifying cluster lifecycle management. AWS states that EKS Anywhere helps automate cluster management on your own infrastructure, which matches the requirement to keep network connectivity and underlying infrastructure on premises. By contrast, EKS on Outposts moves the platform to AWS Outposts hardware, and the self-hosted options increase operational burden because the company would still manage the Kubernetes platform itself. The key distinction here is that the company wants simpler management without giving up control of its current on-premises environment. EKS Anywhere is the AWS service built for exactly that balance.

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For globally distributed consumers of REST APIs, API Gateway edge-optimized endpoints “route requests to the nearest CloudFront edge location, which then forwards them to your API in the [home] Region,” reducing latency for users worldwide and scaling automatically for unknown or spiky traffic. By contrast, Regional endpoints are intended for clients within the same or nearby Region and do not provide global edge acceleration. AWS Lambda provides automatic scaling for serverless backends; latency can be further reduced by right-sizing memory (which also increases CPU) and, when needed, enabling provisioned concurrency to keep functions initialized and eliminate cold starts for critical paths. Using NLBs across Regions is not serverless and adds operational complexity without CloudFront edge acceleration. Therefore, combining API Gateway edge-optimized REST APIs with Lambda meets the requirements of minimal global latency and unknown initial traffic.

A. VPC peering:Creates a fully meshed architecture, which is complex to manage for multiple VPCs.

B. Transit gateway:Simplifies network management by connecting multiple VPCs and on-premises networks via a central hub.

C. PrivateLink:Restricts communication to the application endpoint but may not allow full VPC connectivity.

D. ALB with internet exposure:Not secure or specific to private network communication.

Provisioned IOPS SSD (io1 or io2) is designed for I/O-intensive workloads that require low latency and consistent throughput, which is critical for transactional and production databases. It provides predictable performance, unlike General Purpose SSD, which is burst-based.

Amazon RDSBlue/Green Deploymentsis the ideal solution for upgrading the database version with minimal operational overhead and no data loss. Blue/Green Deployments allows you to create a separate, fully managed " green " environment with the upgraded database version. You can test the new version in the green environment while the " blue " environment continuesserving production traffic. Once testing is complete, you can seamlessly switch traffic to the green environment without downtime.

This solution provides:

Fast, non-disruptive upgrade: Traffic is only switched to the new environment after testing, ensuring zero data loss.

Minimal operational overhead: AWS handles the infrastructure management, reducing manual intervention.

Option A (Manual snapshot): This requires manual intervention and involves more operational overhead.

Option B (Native backup/restore): This approach is more labor-intensive and slower than Blue/Green Deployments.

Option C (DMS): AWS DMS adds unnecessary complexity for a simple version upgrade when Blue/Green Deployments can handle the task more efficiently.

AWS References:

Amazon RDS Blue/Green Deployments

The best answer ismessage batching. AWS documentation explains that SQS batch actions let applications send, receive, or delete up to 10 messages at a time. AWS also notes that batching can be combined with horizontal scaling to improve throughput with fewer requests and connections. That makes batching the most relevant feature for handling sudden traffic increases efficiently. FIFO queues address strict ordering and deduplication, visibility timeout controls how long a received message stays hidden from other consumers, and long polling reduces empty responses and unnecessary polling cost. None of those features directly improve throughput as effectively as batching for traffic spikes.

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To ensure container images are secure and to notify administrators about vulnerabilities, the solution needs (1) a vulnerability scanning capability for container images and (2) a notification mechanism that alerts when findings occur. Amazon Inspector provides automated security assessments and can scan container images stored in Amazon ECR to identify software vulnerabilities and unintended network exposure patterns, producing findings that can be acted upon. Therefore, D addresses the core requirement of detecting vulnerabilities in container images.

To notify administrators with minimal custom work, AWS Config can help by evaluating resources against desired configurations and integrating with notifications through AWS services (for example, via Amazon SNS using Config rules/conformance packs). Using the AWS Config conformance pack for Amazon ECS helps establish a managed set of compliance checks aligned to ECS-related best practices. While Inspector is the system that detects vulnerabilities, Config can be used to enforce and monitor governance controls around the container environment and can trigger notifications when noncompliance is detected. In exam patterns, pairing an Inspector detection capability with a managed governance/notification framework like Config is a common “two-part” answer.

The other options do not meet the requirement: A (privileged mode) can increase risk rather than improve image security; logging does not equal vulnerability detection. C is unrelated because AWS WAF protects web applications at the edge and does not scan container images for CVEs. E is incorrect because Parameter Store stores configuration data and secrets; it does not encrypt container images (ECR encryption at rest is handled by AWS-managed mechanisms and KMS integration, not Parameter Store).

So D provides scanning and B supports managed compliance/notification controls with low operational overhead.

Amazon API Gateway supports multiple API types: REST, HTTP, WebSocket, and gRPC. HTTP APIs are a newer, lightweight option that support JWT authorizers natively, enabling secure, scalable authentication for APIs with JSON Web Tokens.

HTTP APIs can integrate with ALBs as a backend target, providing direct connectivity and simplified API management with JWT authentication built in.

REST APIs support JWT but are more feature-rich and complex, often used for legacy or more complex use cases, and have higher costs and latency. WebSocket APIs are for real-time, bidirectional communication, which is not requested here. gRPC APIs support RPC calls but are less common for public HTTP-based APIs with JWT auth.

Therefore, HTTP API with JWT authorizers is the best fit for this use case.

The lowest-overhead centralized access model is to connect the existingon-premises Active Directoryby usingAD Connectorand then manage AWS account access throughIAM Identity Center permission sets. AWS documentation explains that IAM Identity Center providesone place to assign permissions to multiple AWS accounts, and AD Connector is specifically designed to let AWS services use an existing directory without storing directory information in AWS. This approach preserves the company’s current authentication source and adds centralized authorization across the organization. Creating IAM users or scripting cross-account role assignments would add significant administrative work and weaken the identity model.

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AWS Lake Formation natively supports fine-grained access control, including:

Row-level security

Cell/column-level security

These are implemented through data filters and Lake Formation permissions on governed tables and views. This allows you to centrally define which users or groups can access which rows and which columns, including sensitive data fields, with minimal custom code or maintenance.

Why others are incorrect:

A: IAM alone does not provide row-level or cell-level data security inside Lake Formation tables.

C and D: Using Lambda to scrub or periodically remove sensitive data is complex, error-prone, and high overhead compared to built-in Lake Formation data filters.

Why Option A is Correct:

ElastiCache for Redis: Provides persistent, scalable caching for in-progress transactions and SQL queries. Redis supports data durability and advanced features, making it suitable for transactional workloads.

Integration with Aurora: Easily integrates with the Aurora cluster to improve query performance.

Why Other Options Are Not Ideal:

Option B: CloudFront and S3 are unsuitable for transactional caching. EC2 instance store volumes are ephemeral and lack persistence.

Option C: Memcached does not offer persistence or advanced transactional support, unlike Redis.

Option D: Combining Redis with EC2 instance store is unnecessary; Redis alone meets all caching requirements.

AWS References:

Amazon ElastiCache:AWS Documentation - ElastiCache

Comprehensive and Detailed Step-by-Step Explanation:

The goal is totransfer 500 GB dailyfrom multiple global locationsquicklyintoa single S3 bucketwhile keeping operational complexity low.

Option A:✅

Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.

S3 Transfer Acceleration (S3-TA)allowsfasterglobal uploads by routing traffic throughAmazon CloudFront’s globally distributed edge locations.

Multipart uploadsimprove efficiency bybreaking large filesinto smaller parts, transferring them in parallel.

Low operational complexity: No need for additional resources or manual replication.

Why is this best?It ensureshigh-speed transferswhileminimizing complexity.

The correct answer is to useIAM roles with fine-grained permissions. AWS recommends temporary credentials and least-privilege access instead of shared accounts or long-term access keys. Assigning a separate role to each developer gives the company strong access isolation, simpler auditing, and the ability to restrict each developer to only the AWS actions they need. Shared IAM user credentials are insecure because actions cannot be tied reliably to one person. Long-term access keys increase credential management risk. Amazon Cognito is primarily for application end users, not for granting internal developers administrative access to AWS resources. Using IAM roles is the standard AWS security best practice for controlled workforce access.

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Workload Discovery on AWS is a tool that automatically discovers AWS resources across multiple accounts and Regions and builds visual architecture diagrams that show resource relationships. It is specifically designed to help teams understand and document existing workloads with minimal manual effort.

This service provides multi-account visibility, relationship mapping, and exportable diagrams, which directly satisfies the need to “build and map the relationship details of the various workloads” with the least operational overhead.

Systems Manager Inventory (Option A) focuses on collecting configuration and inventory data (primarily for instances and some resources) and does not generate architecture diagrams or full relationship views.

Step Functions (Option B) and X-Ray (Option D) would require custom collection and manual diagramming and are not purpose-built for environment discovery and mapping.

Amazon SQS is a fully managed message queuing service that reliably decouples and scales microservices, distributed systems, and serverless applications. By using SQS, microservices can communicate asynchronously, handle bursts of traffic, and avoid data loss by buffering messages until they are processed. Deploying the services on ECS with AWS Fargate further reduces operational overhead by removing the need to manage servers, allowing independent scaling of each microservice.

Reference Extract:

" Amazon SQS decouples application components and enables message durability and scaling. AWS Fargate removes the need to manage infrastructure, supporting independent scaling and minimal operational overhead. "

Source: AWS Certified Solutions Architect – Official Study Guide, Microservices and Messaging section.

The correct design is to useRoute 53 failover records, with theprimary record pointing to the ALB and associated with a health check, and thesecondary record pointing to the static website. Route 53 failover routing is designed to return the primary resource when it is healthy and automatically return the secondary resource when the primary becomes unhealthy. The health check is attached to the primary failover record so Route 53 knows when to fail over. You do not need a Lambda function to switch records manually, because Route 53 provides built-in DNS failover. The secondary record does not need its own health check for this pattern. That is whyC and Eare the right combination. (AWS Documentation)

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The current architecture tightly couples image upload, resizing, and storage into a single synchronous workflow handled by EC2 instances. This increases upload latency and degrades user experience. The optimal solution is to decouple upload from processing and offload work to managed services.

Option C allows users’ browsers to upload images directly to Amazon S3 using presigned URLs, bypassing the EC2 web servers entirely. This significantly reduces server load, network hops, and request latency while improving scalability and performance. Presigned URLs also maintain security by granting time-limited, scoped access to S3.

Option D complements this by using S3 Event Notifications to invoke an AWS Lambda function whenever a new image is uploaded. The Lambda function performs the image resizing asynchronously and stores the processed image back in S3. This event-driven, serverless pattern eliminates tight coupling between upload and processing and ensures automatic scaling with minimal operational overhead.

Option A is unsuitable because Glacier is for archival storage and not designed for active uploads or processing. Option B still routes uploads through EC2, maintaining the bottleneck. Option E introduces unnecessary delays and inefficiency by resizing images on a schedule instead of reacting to upload events.

Therefore, C and D together provide the most performant, scalable, and operationally efficient image upload and processing architecture using AWS-native, event-driven services.

CloudFront Signed Cookies:

CloudFront signed cookies allow the company to provide access to premium users while maintaining a single, consistent URL.

This approach is simpler and more scalable than managing presigned URLs for each file.

Incorrect Options Analysis:

Option A: Using EC2 and EBS increases complexity and cost.

Option B: Managing presigned URLs for each file is not scalable.

Option D: CloudFront signed URLs require unique URLs for each file, which does not meet the requirement for a single URL.

The correct answers areA and D. The requirement is to replace third-party middleware with an AWS-native solution that supportsasynchronous communication,exactly-once delivery, and theleast infrastructure management.Amazon SQS FIFO queuesare the best messaging component because FIFO queues are designed for workloads that requireordered processingandexactly-once message deliverysemantics. That makes optionDthe correct messaging choice.

For the compute layer,AWS Lambdaprovides the least infrastructure management because it is a fully managed serverless compute service. Lambda removes the need to manage virtual machines, operating systems, or cluster infrastructure. It integrates naturally with queue-based asynchronous architectures and can be invoked to process messages from SQS while scaling automatically based on queue depth and workload demand. That makes optionAthe best choice for minimizing operational burden.

Option B is incorrect because EC2 instances would require the company to manage instance provisioning, patching, scaling, and availability. Option C is incorrect becauseAmazon SNSis a pub/sub messaging service and does not provide exactly-once delivery guarantees likeSQS FIFO. Option E is incorrect becauseAmazon EKSintroduces Kubernetes management overhead and is not the lowest-management compute option.

AWS best practices recommend using managed messaging and serverless compute to reduce undifferentiated operational work. For a migration that requires asynchronous processing with exactly-once delivery and minimal infrastructure management, the most appropriate architecture isAWS Lambda functionswithAmazon SQS FIFO queues.

To capture DNS query and response data, including response codes, Amazon Route 53 provides query logging, which is the most precise and AWS-supported solution for this requirement.

Option A enables Route 53 query logging, which records detailed information about DNS queries, such as the queried domain, record type, source IP, and DNS response code. These logs are delivered to Amazon CloudWatch Logs, where administrators can search, analyze, and retain them for forensic investigation and root cause analysis.

Option B is incorrect because AWS CloudTrail records API calls to AWS services, not DNS query traffic. Option C provides aggregated metrics (such as query counts and health checks) but does not include per-query response codes. Option D offers best-practice recommendations but does not collect or analyze DNS query data.

Therefore, A is the correct solution because Route 53 query logging provides the detailed, low-level DNS visibility required for troubleshooting and operational analysis.

To track costs by department, you must activate the custom department tag as a cost allocation tag in the AWS Organizations management account. Once activated, Cost Explorer and cost and usage reports can group costs by this tag for all linked accounts. The most operationally efficient way is to activate only the relevant department tag and create a cost and usage report grouped by that tag.

AWS Documentation Extract:

“To use a tag for cost allocation, you must activate it in the AWS Billing and Cost Management console. After activation, you can use the tag to group costs in Cost Explorer and reports.”

(Source: AWS Cost Management documentation)

A, E: aws:createdBy is not related to department cost grouping and is unnecessary.

B: Applying extra filters is optional; D is more direct and operationally efficient.

The correct answer isAbecause the application is alightweight data processing toolthat always completes within3 minutes, which fits well within the execution model ofAWS Lambda. Lambda is a fully managed compute service that eliminates the need to provision or manage servers, clusters, operating systems, or container hosts. Since the company is willing to make any required changes to the image, packaging the workload as aLambda container imageis the option with the least operational overhead.

Lambda is well suited for short-lived, event-driven, or on-demand processing jobs. It automatically scales, charges only for execution time, and removes nearly all infrastructure administration. Compared with container orchestration services, Lambda requires less setup, less monitoring of infrastructure, and no task or cluster capacity planning.

Option B is incorrect becauseAmazon EKS with Fargatestill requires Kubernetes management constructs and introduces more operational complexity than Lambda. Option C is simpler than EKS, butAmazon ECS with Fargatestill involves managing task definitions, services, and networking, which is more overhead than Lambda for a brief processing job. Option D is incorrect because using a custom AMI with AWS Batch introduces more compute management and is unnecessary for a very short, lightweight workload.

AWS guidance favorsserverless serviceswhen the workload is short-lived and infrastructure management should be minimized. Because this job always completes quickly and the company wants the least operational overhead,AWS Lambda with a container imageis the best solution.

The correct security-group design is to allow inbound database access from the EC2 instances’ security group rather than from individual IP addresses. AWS security groups can reference another security group as the source, which is the scalable and recommended approach for application-to-database communication in dynamic environments such as Auto Scaling groups. That way, newly launched instances automatically inherit access without requiring IP-based rule updates. Option A is brittle because private IPs change as instances scale. Option C is not how security-group rules are defined, and option D reverses the traffic direction. Therefore, allowing the database security group to accept traffic from the web-servers security group is the right architecture.

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Amazon EMR is the strongest fit here because AWS describes EMR as a service for processing, analyzing, transforming, and moving large amounts of data, and EMR supports running parallel steps for distributed processing. The question explicitly wantsparallel processingof large semistructured data in S3, which matches EMR well. AWS also documents a supported pattern forloading data from Amazon EMR to Amazon Redshift, and Redshift supports querying semistructured data, which makes EMR plus Redshift a natural enrichment workflow. Athena and Glue can be useful in some analytics patterns, but the requirement for parallel high-volume processing points most directly to EMR.

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EBS Elastic Volumes allow you to dynamically increase storage size, adjust performance, and change volume types without downtime, supporting operational efficiency and scalability for SaaS applications that need to allocate varying storage amounts to customers.

Migrating from EBS to S3 (Option A) is not suitable since S3 is object storage, not block storage, and does not support block-level I/O required by many applications. EFS (Option B) and FSx (Option C) are shared file systems, which might add unnecessary complexity and cost, especially if the application depends on block storage semantics.

Using Lambda to automate Elastic Volumes resizing provides cost efficiency by allocating resources on demand and reduces operational overhead, aligning with AWS operational excellence and cost optimization best practices.

AWS WAF Bot Control is the best fit because the problem is abusive automated traffic submitting unnecessary requests and consuming compute resources. AWS documents that the Bot Control managed rule group is specifically designed to identify and manage bot traffic, including high-confidence bot signatures and common bot categories. This is more targeted than maintaining custom IP blocks and more directly aligned to the behavior described than general IP reputation controls. AWS Shield focuses on DDoS protection, not detailed application-layer bot categorization. Therefore, associating a WAF web ACL with the ALB and enabling Bot Control is the most appropriate solution.

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Explanation (AWS Docs):

DynamoDB Accelerator (DAX) is a fully managed, in-memory cache specifically for DynamoDB. It reduces read load and latency without requiring code changes (only SDK config). This is the least development effort.

“Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers microsecond read performance and requires minimal application changes.”

— Amazon DAX

Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3,Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.

Why Other Options Are Incorrect:

Option B:EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.

Option C:While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.

Option D:Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.

Key AWS Features Used:

AWS Lambda:Real-time serverless processing with integration capabilities for Aurora and S3.

Amazon S3:Cost-effective storage for enriched data.

Amazon Redshift Spectrum:Direct querying of data stored in S3 without loading it into Redshift.

AWS Documentation References:

AWS Lambda Function Overview

Amazon Redshift Spectrum

Processing Streaming Data with Kinesis Data Streams

Amazon S3 Access Grantsis designed to providefine-grained, prefix-level accessto S3 data for users and groups, including identities coming from a corporate directory or IAM Identity Center. AWS documentation explains that S3 Access Grants can map users or groups to specific S3 locations and vend temporary credentials, which makes it far more scalable than building separate IAM users, role-sharing workflows, or presigned URLs for every vendor. IAM Identity Center adds centralized user lifecycle and identity administration. This combination is especially strong when different vendors need access todifferent datasetsin the same data lake. It is also more maintainable and secure than issuing permanent IAM credentials. For auditing, the environment can still rely on AWS logging controls while using Access Grants for access distribution.

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The data isfrequently accessed only for the first 30 daysand then israrely accessedbut must be retained for7 years. That pattern matchesAmazon S3 Standardfor the initial active period and thenS3 Glacier Deep Archivefor long-term retention at the lowest storage cost. AWS guidance positions Glacier Deep Archive for long-lived archive data that is accessed once or twice a year and is ideal for compliance-style retention. EFS and EBS are not cost-effective archival storage choices for this use case. S3 Standard-IA is cheaper than S3 Standard, but Glacier Deep Archive is more cost-optimized for multi-year rare access.

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Amazon S3 is the most cost-effective storage service for large and growing collections of image files, and Amazon CloudFront is the standard way to deliver those objects globally with low latency. AWS documentation states that CloudFront can use an S3 bucket as an origin and cache the objects at edge locations around the world. This reduces latency for viewers and reduces repeated load on the origin. EBS, EFS, and FSx are not the best fit for large-scale internet-facing object delivery because they are not purpose-built global object distribution services. Therefore, S3 plus CloudFront is the correct and most economical architecture for global image delivery at scale.

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This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3- Information on how to store and manage data in Amazon S3.

Amazon Athena- Documentation on using Amazon Athena for querying data stored in S3 using SQL.

UsingAD ConnectorwithAWS IAM Identity Center(formerly AWS Single Sign-On) allows the company to leverage its existingon-premises Active Directoryfor centralized identity management and access control. AD Connector acts as a proxy to the on-premises AD withoutrequiring additional infrastructure or complex setup. This solution integrates seamlessly with AWS, allowing the development team to use their existing AD credentials to access AWS resources across multiple accounts managed by AWS Organizations. The permissions for AWS resources can be managed centrally through IAM Identity Center by configuring permission sets.

This solution provides:

Least operational overhead: AD Connector is fully managed, and IAM Identity Center allows centralized management of permissions across accounts.

Secure access: The solution complies with security policies by using existing AD authentication mechanisms.

Option A (AWS Managed AD): Setting up a fully managed AWS AD and establishing a trust is more complex and involves additional operational overhead.

Option B (IAM Users): Manually managing IAM users and permissions is less scalable and increases operational complexity.

Option D (Cognito): Amazon Cognito is more suited for user-facing applications rather than internal identity management for AWS resources.

AWS References:

AD Connector with IAM Identity Center

AWS IAM Identity Center