DOP-C02 Questions and Answers

 To optimize the Macie costs for the account without compromising the account’s functionality, the DevOps engineer needs to exclude S3 buckets that do not contain sensitive data from automated discovery. S3 buckets that contain CloudTrail logs are unlikely to have sensitive data, and Macie charges for scanning and monitoring data in S3 buckets. Therefore, excluding S3 buckets that contain CloudTrail logs from automated discovery can reduce Macie costs. Similarly, configuring discovery jobs to include S3 objects based on the last modified criterion can also reduce Macie costs, as it will only scan and monitor new or updated objects, rather than all objects in the bucket.

The current design colocates the web tier, database, and cache on the same EC2 instances. This causes resource contention and makes it difficult to scale each layer independently. The correct solution is to separate these concerns into independently scalable managed services.

Option D uses an Application Load Balancer (ALB) in front of an Auto Scaling group for the web application. ALB is designed for HTTP/HTTPS traffic, supports path-based and host-based routing, and can spread load evenly across multiple instances and Availability Zones, eliminating the issue where one instance is overloaded.

The database is migrated to Amazon Aurora with Multi-AZ , which provides high availability, automated failover, and managed backups. Aurora offloads database management and ensures consistent performance.

For caching, Option D introduces Amazon ElastiCache for Redis , a managed in-memory cache that is purpose-built for low-latency reads, independent scaling, and high availability through replication and clustering.

Option A and C misuse NLB for HTTP traffic or for Redis exposure and introduce unnecessary complexity. Option B keeps Redis on EC2 (via NLB + ASG in a single AZ), which reduces availability and does not leverage managed caching.

Thus, Option D best separates tiers and improves performance and availability.

Deployment of config.txt file based on the appspec.yml:

The appspec.yml file specifies that config/config.txt should be copied to /usr/local/src/config.txt.

The source: / directive in the appspec.yml indicates that the entire directory structure starting from the root of the application source should be copied to the specified destination, which is /var/www/html.

Result of the Deployment:

The config.txt file will be specifically deployed to /usr/local/src/config.txt as per the explicit file mapping.

The entire directory structure including application/web will be copied to /var/www/html, but this does not include config/config.txt since it has a specific destination defined.

Thus, the config.txt file will be deployed only to /usr/local/src/config.txt.

Therefore, the correct answer is:

C. The config.txt file will be deployed to only /usr/local/src/config.txt.

A Lambda function in one account can mount a file system in a different account. For this scenario, you configure VPC peering between the function VPC and the file system VPC. https://docs.aws.amazon.com/lambda/latest/dg/services-efs.html

https://aws.amazon.com/ru/blogs/storage/mount-amazon-efs-file-systems-cross-account-from-amazon-eks/

1. Need to update the file system policy on EFS to allow mounting the file system into Account B.

## File System Policy

$ cat file-system-policy.json

{

" Statement " : [

{

" Effect " : " Allow " ,

" Action " : [

" elasticfilesystem:ClientMount " ,

" elasticfilesystem:ClientWrite "

],

" Principal " : {

" AWS " : " arn:aws:iam:: < aws-account-id-A > :root " # Replace with AWS account ID of EKS cluster

}

}

]

}

2. Need VPC peering between Account A and Account B as the pre-requisite

3. Need to assume cross-account IAM role to describe the mounts so that a specific mount can be chosen.

CodeDeploy deployment failures must be diagnosed using deployment-specific logs and related application logs. CodeDeploy writes detailed logs on the target EC2 instances under /opt/codedeploy-agent/deployment-root/ (for example in logs and deployment-logs subdirectories). These logs include information about lifecycle events (BeforeInstall, AfterInstall, ApplicationStart, etc.), script execution failures, permissions issues, and any exit codes returned by deployment hooks.

Option C correctly points to CodeDeploy agent logs on the EC2 instances as the primary source for root cause analysis. Additionally, application-level logs streamed to Amazon CloudWatch Logs help validate whether the application itself is working correctly after deployment. For complex, multi-tier applications, AWS X-Ray can trace requests end to end, helping determine if issues are caused by downstream dependencies instead of the deployment process.

Options A, B, and D focus on network flow, generic performance, or high-level checks, none of which provide sufficient, deployment-level detail for CodeDeploy. Trusted Advisor and AWS Health do not surface step-by-step deployment log data. Therefore, the combination of CodeDeploy logs on the instances, CloudWatch Logs, and (optionally) X-Ray is the correct troubleshooting approach.

To restore the functionality of the Auto Scaling group after the AMI was deleted, the DevOps engineer needs to create a new AMI and update the Auto Scaling group to use it. The DevOps engineer can create a new AMI by copying the most recent public AMI of the operating system that the EC2 instances use. This will ensure that the new AMI has the same operating system as the custom AMI that was deleted. The DevOps engineer can then create a new launch template that uses the new AMI and update the Auto Scaling group to use the new launch template. This will allow the Auto Scaling group to launch new instances with the new AMI.

Step 1: Using AWS CloudFormation with ECS Blue/Green Deployments

The requirement is to implement an ECS blue/green deployment where traffic is shifted gradually. AWS CodeDeploy supports such blue/green deployments with predefined configurations, like ECSLinear10PercentEvery1Minute, which shifts 10% of traffic every minute.

Action: Use the AWS::CodeDeployBlueGreen transform and the appropriate hooks in the CloudFormation template. The ECSLinear10PercentEvery1Minute deployment configuration meets the requirement of shifting 10% of traffic every minute.

Why: The transform and hook parameters in CloudFormation are essential for configuring the blue/green deployment with the desired traffic-shifting behavior.

To prevent ApplicationA from being installed on the additional instance, the deployment group configuration needs to be more specific. By changing the current single tag group to include only the Environment=Production tag and adding another single tag group that includes only the Name=ApplicationA tag, the deployment process will target only the instances that match both tag groups. This ensures that only instances intended for ApplicationA with the correct environment and name tags will receive the deployment, thus excluding the additional instance with the Department=Marketing and Name=ApplicationB tags.

AWS CodeDeploy Documentation: Working with instances for CodeDeploy

AWS CodeDeploy Documentation: Stop a deployment with CodeDeploy

Stack Overflow Discussion: CodeDeploy Deployment failed to stop Application

It allows the company to detect potentially compromised EC2 instances, suspicious network activity, and unusual API activity in its existing AWS accounts and in any AWS accounts that the company creates in the future using Amazon GuardDuty. It also provides a solution for automatically adding future AWS accounts to GuardDuty by configuring GuardDuty to add newly created AWS accounts by invitation and to send invitations to the existing AWS accounts.

To meet the requirements, the company needs to create a solution that alerts the Senior Manager when the creation of resources approaches the service limits for the account with the least amount of development effort. The company can use AWS Trusted Advisor, which is a service that provides best practice recommendations for cost optimization, performance, security, and service limits. The company can deploy an AWS Lambda function that refreshes Trusted Advisor checks, and configure an Amazon CloudWatch Events rule to run the Lambda function periodically. This will ensure that Trusted Advisor checks are up to date and reflect the current state of the account. The company can then create another CloudWatch Events rule with an event pattern matching Trusted Advisor events and a target Lambda function. The event pattern can filter for events related to service limit checks and their status. The target Lambda function can notify the Senior Manager via a third-party API call if the event indicates that the account is approaching or exceeding a service limit.

The startup delay occurs because large container images must be pulled from Amazon ECR when pods are scheduled, especially on newly added nodes that do not have cached images. To consistently reduce pod startup time to seconds, container images must be prefetched directly onto the worker nodes that run the pods, not the EKS control plane.

Amazon EKS control plane nodes are fully managed by AWS and cannot be accessed or modified using AWS Systems Manager. Therefore, any solution that attempts to run Systems Manager commands on control plane nodes is invalid. Prefetching must target the EKS worker nodes (EC2 instances in managed node groups) where container images are actually stored and used.

Option C correctly creates an IAM role that allows Amazon EventBridge to invoke AWS Systems Manager to run commands on the EKS worker nodes. By using Systems Manager State Manager associations with node tags , the automation dynamically targets both existing nodes and newly added nodes that inherit the same tags. This ensures that container images are pulled immediately when a new image is pushed to ECR or when new nodes join the cluster.

Option B incorrectly targets nodes based on machine size, which is not reliable for lifecycle automation. Options A and D incorrectly reference control plane nodes, which cannot be managed with Systems Manager.

Therefore, Option C is the correct and AWS-aligned solution to ensure fast pod startup times across all nodes.

Option A is correct because removing the AdministratorAccess policy and assigning the ReadOnlyAccess managed IAM policy to the developer role is a valid way to prevent the developers from making any manual changes to the deployed resources. The AdministratorAccess policy grants full access to all AWS resources and actions, which is not necessary for the developers. The ReadOnlyAccess policy grants read-only access to most AWS resources and actions, which is sufficient for the developers to view the status of their stacks. Instructing the developers to use the CloudFormationDeployment role as a CloudFormation service role when they deploy new stacks is also a valid way to ensure that only CloudFormation can use the new role. A CloudFormation service role is an IAM role that allows CloudFormation to make calls to resources in a stack on behalf of the user1. The user can specify a service role when they create or update a stack, and CloudFormation will use that role’s credentials for all operations that are performed on that stack1.

Option B is incorrect because updating the trust of CloudFormationDeployment role to allow the developer IAM role to assume the CloudFormationDeployment role is not a valid solution. This would allow the developers to manually assume the CloudFormationDeployment role and perform actions on the deployed resources, which is not what the company wants. The trust of CloudFormationDeployment role should only allow the cloudformation.amazonaws.com AWS principal to assume the role, as in option D.

Option C is incorrect because configuring the IAM user to be able to get and pass the CloudFormationDeployment role if cloudformation actions for resources is not a valid solution. This would allow the developers to manually pass the CloudFormationDeployment role to other services or resources, which is not what the company wants. The IAM user should only be able to pass the CloudFormationDeployment role as a service role when they create or update a stack with CloudFormation, as in option A.

Option D is correct because updating the trust of CloudFormationDeployment role to allow the cloudformation.amazonaws.com AWS principal to perform the iam:AssumeRole action is a valid solution. This allows CloudFormation to assume the CloudFormationDeployment role and access resources in other services on behalf of the user2. The trust policy of an IAM role defines which entities can assume the role2. By specifying cloudformation.amazonaws.com as the principal, you grant permission only to CloudFormation to assume this role.

Option E is incorrect because instructing the developers to assume the CloudFormationDeployment role when they deploy new stacks is not a valid solution. This would allow the developers to manually assume the CloudFormationDeployment role and perform actions on the deployed resources, which is not what the company wants. The developers should only use the CloudFormationDeployment role as a service role when they deploy new stacks with CloudFormation, as in option A.

Option F is correct because adding an IAM policy to CloudFormationDeployment that allows cloudformation:* on all resources and adding a policy that allows the iam:PassRole action for ARN of CloudFormationDeployment if iam:PassedToService equals cloudformation.amazonaws.com are valid solutions. The first policy grants permission for CloudFormationDeployment to perform any action with any resource using cloudformation.amazonaws.com as a service principal3. The second policy grants permission for passing this role only if it is passed by cloudformation.amazonaws.com as a service principal4. This ensures that only CloudFormation can use this role.

This solution will meet the requirements because it will reduce the number of errorless records that are sent to the dead-letter queue. When you configure the setting to split the batch when an error occurs, Lambda will retry only the records that caused the error, instead of retrying the entire batch. This way, the records that have no data errors and have already been processed by the legacy REST API will not be retried and sent to the dead-letter queue unnecessarily.

https://docs.aws.amazon.com/lambda/latest/dg/with-kinesis.html

ALB supports weighted target groups for traffic splitting between versions without DNS-level changes. By attaching multiple target groups (old & beta) to the same ALB listener rule with weights (90/10), partial traffic testing can occur seamlessly. This is the recommended least-change method per Application Load Balancer advanced request routing documentation.

The question focuses on detecting supply chain attacks that bypass CI/CD controls and result in malicious runtime behavior in the deployed serverless application. This requires runtime threat detection , not just static scanning or infrastructure validation.

Amazon GuardDuty is AWS’s managed threat detection service. GuardDuty now includes Lambda Protection , which analyzes Lambda function behavior, including network calls, IAM API usage, and other signals to detect compromised functions, crypto-mining, data exfiltration, and other malicious activity. GuardDuty automatically ingests CloudTrail, VPC Flow Logs (if enabled), and other telemetry without requiring code changes or agents. Findings are published and can be routed to Amazon EventBridge for near real-time notifications or automated remediation.

Option A (AWS WAF) only inspects HTTP requests to API Gateway and is focused on known bad patterns, not deeper behavioral anomalies. Option C (CloudFormation Guard) is focused on infrastructure-as-code validation in the pipeline, which explicitly does not help once malicious code is deployed. Option D (Network Firewall with Emerging Threats rules) protects VPC traffic, but serverless components like Lambda and API Gateway may not all traverse that firewall, and it is not targeted at function-level behavior.

Hence, enabling GuardDuty with Lambda Protection and using EventBridge notifications is the correct solution.

https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-hooks.html

When you create a pipeline from CodePipeline during the step-by-step it creates a CloudWatch Event rule for a given branch and repo

like this:

{

" source " : [

" aws.codecommit "

],

" detail-type " : [

" CodeCommit Repository State Change "

],

" resources " : [

" arn:aws:codecommit:us-east-1:xxxxx:repo-name "

],

" detail " : {

" event " : [

" referenceCreated " ,

" referenceUpdated "

],

" referenceType " : [

" branch "

],

" referenceName " : [

" master "

]

}

}

https://docs.aws.amazon.com/codepipeline/latest/userguide/pipelines-trigger-source-repo-changes-console.html

 Use AWS Step Functions to Orchestrate Processing:

AWS Step Functions allow you to build distributed applications by combining AWS Lambda functions or other AWS services into workflows.

Decoupling the processing into Step Functions tasks enables you to retry individual steps without reprocessing the entire record.

 Architectural Steps:

Create a web application to pass records to AWS Step Functions:

The web application can be a simple frontend that receives input and triggers the Step Functions workflow.

Define a Step Functions state machine:

Each step in the state machine represents a processing stage. If a step fails, Step Functions can retry the step based on defined conditions.

Use AWS Lambda functions:

Lambda functions can be used to handle each processing step. These functions can be stateless and handle specific tasks, reducing the complexity of error handling and reprocessing logic.

 Operational Efficiency:

Using Step Functions and Lambda improves operational efficiency by providing built-in error handling, retries, and state management.

This architecture scales automatically and isolates failures to individual steps, ensuring only failed steps are retried.

https://docs.aws.amazon.com/IAM/latest/UserGuide/reference_policies_examples_aws_deny-ip.html

Creating an AWS CodeCommit repository for each project, using the main branch for production code, and creating a testing branch for code deployed to testing will meet the requirements. AWS CodeCommit is a managed revision control service that hosts Git repositories and works with all Git-based tools1. By using feature branches to develop new features and pull requests to merge code to testing and main branches, the developers can avoid code conflicts and lost work, and also implement code reviews and approvals. Option B is incorrect because creating another S3 bucket for each project for testing code and using an AWS Lambda function to promote code changes between testing and production buckets will not provide the benefits of revision control, such as tracking changes, branching, merging, and collaborating. Option C is incorrect because using the main branch for production and test code with different deployment pipelines for each environment will not allow the developers to test their code changes before deploying them to production. Option D is incorrect because enabling versioning and branching on each S3 bucket will not work with Git-based tools and will not provide the same level of revision control as AWS CodeCommit. References:

AWS CodeCommit

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 182)

RDS Multi-AZ cluster deployments with cross-Region read replicas support near real-time replication ( < 1 min RPO) and fast promotion ( < 10 min RTO). Route 53 CNAME failover redirects application traffic automatically. This is the AWS-recommended DR pattern.

The security team’s requirements emphasize governance, approval control, versioning, and automation with minimal operational overhead. AWS Control Tower natively supports guardrail enablement through the AWS::ControlTower::EnableControl CloudFormation resource, which can target entire organizational units. Managing these resources as CloudFormation templates stored in a Git repository provides built-in version control, change review, and rollback capabilities through standard Git workflows.

Option C implements a GitOps-style approach. Each guardrail is defined declaratively in CloudFormation, scoped at the OU level, ensuring consistent enforcement across all accounts in that OU. Storing the templates in a CodeConnections-compatible Git repository allows the security team to review, approve, and audit changes through pull requests. By configuring AWS CodePipeline in the security team’s account and triggering it automatically via an Amazon EventBridge rule on repository merges, only approved changes are deployed. This ensures that new guardrails are enabled only after explicit approval and that the deployment process is fully automated.

Option A lacks pipeline orchestration and approval workflows. Option B requires manual pipeline invocation and per-account configuration, increasing operational overhead. Option D relies on S3 as the source of truth, which lacks native version control and approval mechanisms compared to Git.

Therefore, Option C provides the most operationally efficient, secure, and auditable solution aligned with AWS Control Tower automation best practices.

Amazon ECR enhanced scanning uses Amazon Inspector for vulnerability detection.

EventBridge can capture Inspector scan findings.

Lambda can process scan findings and reject manual approval if critical vulnerabilities exist.

Options A and C use incorrect or less integrated services (basic scanning or Detective).

Option B adds unnecessary complexity with SBOM and Athena.

The “fan-out” batch build type in AWS CodeBuild allows running multiple builds in parallel for different subsets of tests, reducing overall test execution time. CodeBuild then aggregates the results into a single consolidated report. This approach is detailed in the AWS documentation under “Batch builds and parallel execution with CodeBuild.”

https://aws.amazon.com/blogs/mt/monitor-changes-and-auto-enable-logging-in-aws-cloudtrail/

This solution will meet the requirements because it will create a temporary copy of the production database using a snapshot, run the integration tests on the copy, and delete the copy after the tests are done. This way, the production database will not be affected by the code revisions, and the DevOps team can test the changes before deploying them to production. A buildspec file is a YAML file that contains the commands and settings that CodeBuild uses to run a build1. The buildspec file can specify the steps to restore the DB cluster from a snapshot, run the integration tests, and drop the restored database2

https://aws.amazon.com/blogs/mt/how-to-set-up-aws-opsworks-stacks-auto-healing-notifications-in-amazon-cloudwatch-events/

The company requires an organization-wide, centralized, and automated solution to detect sensitive data in Amazon S3, aggregate findings in one location, and quarantine affected objects with minimal operational overhead. AWS provides a native service specifically designed for this purpose: Amazon Macie .

Option A is essential because Amazon Macie automatically discovers and classifies sensitive data such as PII in S3 buckets using managed machine learning models. When enabled at the organization level , Macie scans buckets across all accounts and Regions. Integrating Macie with AWS Security Hub centralizes all findings in a single dashboard, allowing the company’s security officer to review and manage sensitive data alerts across the organization without building custom aggregation pipelines.

Detection alone is not sufficient; remediation is also required. Option C completes the solution by using Amazon EventBridge , which natively receives Macie findings in near real time. An EventBridge rule can trigger a Lambda function whenever Macie identifies sensitive data. The Lambda function can then copy the affected object to a quarantine S3 bucket and delete the original object, meeting the remediation requirement automatically and consistently.

Option D requires custom sensitive data detection logic, which is complex, error-prone, and unnecessary given Macie’s capabilities. Option E is invalid because SCPs cannot inspect or move data. Option B does not detect sensitive information.

Therefore, A and C together provide the most efficient, scalable, and AWS-recommended solution.

Canary deployments gradually shift traffic (10% at first), enabling monitoring before full rollout.

Monitoring TargetResponseTime aligns with detecting increased latency, a key issue reported.

Alarms tied to the deployment group trigger automatic rollback on threshold breach.

UnHealthyHostCount and TargetConnectionErrorCount are less specific to response time issues.

Linear deployment is valid but canary is preferred for quick detection.

API Gateway supports canary deployment on a deployment stage before you direct all traffic to that stage. A parallel environment means we will create a new ALB and a target group that will target a new set of EC2 instances on which the newer version of the app will be deployed. So the canary setting associated to the new version of the API will connect with the new ALB instance which in turn will direct the traffic to the new EC2 instances on which the newer version of the application is deployed.

AWS X-Ray provides distributed tracing, which allows visualization of latencies and errors within microservices, pinpointing bottlenecks or delays.

Instrumenting application code with the X-Ray SDK and running the X-Ray daemon as a DaemonSet in EKS ensures tracing data is collected cluster-wide.

Container Insights (Option B) provides resource-level metrics but not detailed request tracing.

CloudWatch alarms and alerts (Option C) detect symptoms but don ' t provide root cause tracing.

Increasing timeouts (Option D) only masks the issue and does not diagnose it.

 Ensure that Amazon GuardDuty is Enabled:

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior.

It can detect port scans and generate findings for these events.

 Create an Amazon CloudWatch Alarm for Detected EC2 and Port Scan Findings:

Configure GuardDuty to monitor for port scans and other threats.

Create a CloudWatch alarm that triggers when GuardDuty detects port scan activities.

 Connect the Alarm to the SNS Topic:

The CloudWatch alarm should be configured to send notifications to the SNS topic subscribed by the security team.

This setup ensures that the security team receives near-real-time notifications when a port scan is detected on the EC2 instances.

Example configuration steps:

Enable GuardDuty and ensure it is monitoring the relevant AWS accounts.

Create a CloudWatch alarm:

{

" AlarmName " : " GuardDutyPortScanAlarm " ,

" MetricName " : " ThreatIntelIndicator " ,

" Namespace " : " AWS/GuardDuty " ,

" Statistic " : " Sum " ,

" Dimensions " : [

{

" Name " : " FindingType " ,

" Value " : " Recon:EC2/Portscan "

}

],

" Period " : 300,

" EvaluationPeriods " : 1,

" Threshold " : 1,

" ComparisonOperator " : " GreaterThanOrEqualToThreshold " ,

" AlarmActions " : [ " arn:aws:sns:region:account-id:SecurityAlerts " ]

}

Amazon DevOps Guru is a fully managed service that uses machine learning to detect anomalous application behavior and provides insights with recommended remediation actions to improve operational performance.

Enabling DevOps Guru (Option A) covers many AWS resources automatically and reduces manual analysis.

Configuring SNS notifications (Option B) allows the team to be proactively alerted about important events.

Options C, D, and E require manual data setup, complex querying, and dashboard/reporting maintenance, which increase operational overhead. Therefore, the combination of enabling DevOps Guru and setting up notifications achieves the goals with minimal effort.

Step 1: Understanding Lambda Permissions and AWS Config

The custom AWS Config rule evaluates resources and invokes an AWS Lambda function when a compliance check is triggered. For AWS Config to invoke the Lambda function, it requires permission to do so.

Issue: The Lambda function fails to execute because AWS Config doesn ' t have permission to invoke it.

Action: Modify the resource-based policy of the Lambda function to grant AWS Config permission to invoke the Lambda function.

Why: Without this permission, AWS Config cannot trigger the Lambda function, which is why the evaluation fails.

The Amazon SQS service publishes several standard CloudWatch metrics by default, including ApproximateNumberOfMessagesVisible, which represents the number of messages available for retrieval from the queue (i.e., the number of messages waiting to be processed). This is the primary metric to monitor queue backlog and processing delays.

Using ApproximateNumberOfMessagesVisible to monitor the visible messages in the queue gives a direct and near real-time indication of processing delays or backlog.

CloudWatch metrics are automatically collected and available without the need to create custom metrics or Lambda functions, which increases operational efficiency by reducing complexity and maintenance overhead.

Setting a CloudWatch alarm on this metric with a static threshold and a reasonable evaluation period (such as 1 hour) is sufficient to alert the team when the queue grows beyond an expected size.

The alarm can be directly configured to send notifications to an existing SNS topic, maintaining seamless integration with the team’s alerting mechanisms.

The ApproximateNumberOfMessagesDelayed metric refers to messages that are delayed and not available for processing yet due to delay settings, which is not the direct backlog that causes business process delays.

Options C and D introduce unnecessary complexity by requiring Lambda functions and custom metrics or scheduled invocations, which reduce operational efficiency compared to using built-in CloudWatch metrics and alarms.

Reference from AWS Official Documentation and Study Guide:

Amazon SQS Monitoring with CloudWatch Metrics: " Amazon SQS automatically sends metrics to CloudWatch, including ApproximateNumberOfMessagesVisible, which shows the number of messages available for retrieval. " (Source: Amazon SQS Monitoring - AWS Documentation)

Setting CloudWatch Alarms for SQS Queues: " You can create CloudWatch alarms on SQS metrics such as ApproximateNumberOfMessagesVisible to receive notifications when the queue size exceeds a threshold. " (Source: Amazon CloudWatch Alarms - AWS Documentation)

AWS DevOps Engineer Professional Exam Guide: " Efficient alerting for queue backlogs should leverage native CloudWatch metrics to minimize operational overhead. " (Source: Official AWS Certified DevOps Engineer Professional Study Guide)

Amazon FSx for NetApp ONTAP provides NetApp ONTAP features in AWS, including SnapMirror replication and storage efficiencies like deduplication and compression. Create FSx for ONTAP in each Region and use SnapMirror from on-prem ONTAP to each Region for efficient, incremental replication. Regions can serve data independently of on-prem availability once replicated.

EventBridge is needed to detect the database failure. Lambda is needed to promote the replica as it ' s in another Region (manual promotion, otherwise). Storing and updating the endpoint in Parameter store is important in updating the application. Look at High Availability section of Aurora FAQ: https://aws.amazon.com/rds/aurora/faqs/

Verify No SCP Blocking Access:

Ensure that no Service Control Policy (SCP) is blocking access for developers to the S3 bucket. SCPs are applied at the organization or organizational unit (OU) level in AWS Organizations and can restrict what actions users and roles in the affected accounts can perform.

Verify No IAM Policy Permissions Boundaries Blocking Access:

IAM permissions boundaries can limit the maximum permissions that a user or role can have. Verify that these boundaries are not restricting access to the S3 bucket.

Make Necessary Changes to SCP and IAM Policy Permissions Boundaries:

Adjust the SCPs and IAM permissions boundaries if they are found to be the cause of the access issue. Make sure these changes are reflected in the code maintained in the AWS CodeCommit repository.

Invoke Deployment Through CloudFormation:

Commit the updated policies to the CodeCommit repository.

Use AWS CloudFormation to deploy the changes across the relevant accounts and resources to ensure that the updated permissions are applied consistently.

By ensuring no SCPs or IAM policy permissions boundaries are blocking access and making necessary changes if they are, the DevOps engineer can resolve the access issue for developers trying to access the S3 bucket.

The requirement is to update the infrastructure to ensure that only the Lambda function’s execution role can access the values in Secrets Manager. The solution must apply the principle of least privilege, which means granting the minimum permissions necessary to perform a task.

To do this, the DevOps engineer needs to use the following steps:

Create a KMS customer managed key that trusts Secrets Manager and allows the Lambda function’s execution role to decrypt. A customer managed key is a symmetric encryption key that is fully managed by the customer. The customer can define the key policy, which specifies who can use and manage the key. By creating a customer managed key, the DevOps engineer can restrict the decryption permission to only the Lambda function’s execution role, and prevent other principals from accessing the secret values. The customer managed key also needs to trust Secrets Manager, which means allowing Secrets Manager to use the key to encrypt and decrypt secrets on behalf of the customer.

Update Secrets Manager to use the new customer managed key. Secrets Manager allows customers to choose which KMS key to use for encrypting each secret. By default, Secrets Manager uses the default KMS key for Secrets Manager, which is a service-managed key that is shared by all customers in the same AWS Region. By updating Secrets Manager to use the new customer managed key, the DevOps engineer can ensure that only the Lambda function’s execution role can decrypt the secret values using that key.

Ensure that the Lambda function’s execution role has the KMS permissions scoped on the resource level. The Lambda function’s execution role is an IAM role that grants permissions to the Lambda function to access AWS services and resources. The role needs to have KMS permissions to use the customer managed key for decryption. However, to apply the principle of least privilege, the role should have the permissions scoped on the resource level, which means specifying the ARN of the customer managed key as a condition in the IAM policy statement. This way, the role can only use that specific key and not any other KMS keys in the account.

https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html

Update the buildspec.yml file to log in to the ECR repository by using the aws ecr get-login-password AWS CLI command to obtain an authentica-tion token. Update the docker login command to use the authentication token to access the ECR repository.

This is the correct solution. The aws ecr get-login-password AWS CLI command retrieves and displays an authentication token that can be used to log in to an ECR repository. The docker login command can use this token as a password to authenticate with the ECR repository. This way, the CodeBuild project can push and pull images from the ECR repository without any errors. For more information, see Using Amazon ECR with the AWS CLI and get-login-password.

AWS recommends Application Auto Scaling for ECS services to dynamically adjust the number of running tasks based on Amazon SQS queue metrics such as ApproximateNumberOfMessagesVisible. By using target tracking scaling policies, ECS on Fargate scales automatically when the queue backlog grows and scales down when traffic decreases — a fully managed, cost-efficient solution (see ECS Service Auto Scaling Developer Guide).

You can run custom recipes manually, but the best approach is usually to have AWS OpsWorks Stacks run them automatically. Every layer has a set of built-in recipes assigned each of five lifecycle events—Setup, Configure, Deploy, Undeploy, and Shutdown. Each time an event occurs for an instance, AWS OpsWorks Stacks runs the associated recipes for each of the instance ' s layers, which handle the corresponding tasks. For example, when an instance finishes booting, AWS OpsWorks Stacks triggers a Setup event. This event runs the associated layer ' s Setup recipes, which typically handle tasks such as installing and configuring packages

Amazon S3 can generate server access logs that record detailed information about each request, including requester, bucket, key, operation, time, and status. These logs are written as objects to an S3 bucket. To analyze access patterns, the simplest and most serverless approach is to use Amazon Athena directly on those logs without building ingestion pipelines or databases.

Option B enables S3 server access logging and then creates an Athena external table over the log bucket. AWS provides standard log formats and even example schemas for S3 access logs. The analytics team can run ad hoc SQL queries to count the number of accesses per object per time period, filter by user, and perform aggregations, all without provisioning compute or managing databases.

Option A requires ingesting logs into Aurora, which adds ETL complexity and ongoing database management. Option C requires a Lambda function for every access event plus DB writes, which is more complex and potentially expensive at scale. Option D uses CloudWatch Logs and Managed Flink, which is more suited for streaming analytics and is significantly more complex than necessary for monthly summary reports.

Therefore, Option B provides the required analysis with the least development and operational effort.

AWS CodeDeploy natively supports automatic rollback based on CloudWatch alarms . To use this capability, you define an alarm that indicates unhealthy behavior (for example, increasing application error rate) and attach that alarm to the deployment group in CodeDeploy. If the alarm goes into ALARM state during or after a deployment, CodeDeploy automatically rolls back to the last known good revision when auto rollback is enabled.

Option A correctly implements this pattern:

Derive a CloudWatch metric from application logs (e.g., count of error-level log entries or HTTP 5xx responses).

Create a CloudWatch alarm on that metric with appropriate thresholds.

Configure the deployment group to use the alarm as part of its alarm configuration and enable auto rollback .

Option B uses CodeDeploy agent logs, which may not directly reflect true application health and couples rollback logic tightly to deployment internals rather than application behavior. Options C and D implement custom rollback logic with Lambda and EventBridge, which re-create behavior that CodeDeploy already provides natively and add unnecessary operational and code complexity.

Therefore, Option A is the simplest and most aligned with built-in CodeDeploy features for automated rollback based on application errors.

Create an EC2 Image Builder Pipeline that Uses a Container Recipe to Build the Image:

EC2 Image Builder simplifies the creation, maintenance, validation, and sharing of container images.

By using a container recipe, you can define the base image, components, and validation tests for your container image.

Configure the Pipeline to Distribute the Image to Amazon Elastic Container Registry (Amazon ECR) Repositories in All Three Regions:

Amazon ECR provides a secure, scalable, and reliable container registry.

Configuring the pipeline to distribute the image to ECR repositories in us-west-2, us-east-2, and eu-central-1 ensures that the image is available in all required regions.

Configure the Pipeline to Run Weekly:

Setting the pipeline to run on a weekly schedule ensures that the base image is regularly updated and published, incorporating any new security configurations or updates.

By using EC2 Image Builder to automate the creation and distribution of the container image, the solution ensures that the base image is consistently maintained and available across multiple regions with minimal management overhead.

Option A is the most correct because it provides both : (1) automated patching and (2) compliance monitoring/enforcement across a fleet, using AWS-native services built for exactly this purpose.

AWS Systems Manager Patch Manager is designed to automate patching of managed instances using patch baselines , maintenance windows (or on-demand), and it produces compliance status for patching. It’s the standard AWS service to apply OS/security patches at scale without SSH’ing into instances.

AWS Config can be used to evaluate and track compliance over time against defined rules, giving centralized visibility and continuous compliance assessment. With remediation , Config can invoke Systems Manager Automation documents to correct non-compliant resources or trigger patch actions (depending on the rule/remediation design). This meets the “monitor and enforce” requirement.

Why the other options don’t meet requirements as well:

B is manual, doesn’t scale well, and increases operational risk (key management, human error). It’s not “automated monitoring and enforcement.”

C (replacing instances with new AMIs) can be part of an immutable infrastructure strategy, but by itself it does not provide compliance monitoring across the current fleet, and scaling policies based on CloudWatch metrics are unrelated to patch compliance. Also, patch cadence would depend on AMI pipelines and instance rotation rather than direct compliance enforcement.

D is operationally heavy and mismatched: CloudTrail records API activity; it does not natively provide “patch compliance” status for instance OS packages. Recreating instances via CloudFormation for every patch is not an efficient or standard enforcement mechanism for patch compliance.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The correct solution is to allow the production ECS cluster in the separate AWS account to pull images directly from the Amazon ECR repository in the main AWS account by using a cross-account repository policy and the required private networking endpoints.

Option D satisfies both requirements:

Cross-account image access is provided by the repository policy on the Amazon ECR repository in the main AWS account.

Private image download is provided by Amazon ECR VPC endpoints together with the Amazon S3 gateway endpoint, which are required for private image pulls without using the public internet.

The ECS task execution role in the production account also needs permission to pull the image.

Why the other options are incorrect:

A. Creating an ECR repository in the separate account is unnecessary for this requirement, and the option does not correctly describe the image pull model from the source repository.

B. This option addresses cross-account access, but it does not include the VPC endpoints and S3 gateway endpoint required for private connectivity.

C. Cross-account replication copies images to another account, but this option does not address the requirement for the production cluster to download images over a private connection.

service catalog uses stacksets and can enforce tag and restrict resources AWS Customer case with tag enforcement https://aws.amazon.com/ko/blogs/apn/enforce-centralized-tag-compliance-using-aws-service-catalog-amazon-dynamodb-aws-lambda-and-amazon-cloudwatch-events/ And Youtube video showing how to restrict resources per user with portfolio https://www.youtube.com/watch?v=LzvhTcqqyog

This hook runs before the new application version is installed on the replacement instances. This is the best place to run the script because it ensures that the license file is downloaded and installed before the replacement instances start to handle request traffic. If you use any other hook, you may encounter errors or inconsistencies in your application.

Container restart failures require detailed observability into pod-level, node-level, and container-level metrics and logs. CloudWatch Container Insights is purpose-built for Kubernetes operational diagnostics and provides granular visibility into CPU, memory, network I/O, disk I/O, container restarts, OOM kills, throttling, pod lifecycle issues, and Kubernetes control plane behaviors.

The CloudWatch Observability add-on deploys Fluent Bit and the CloudWatch Agent directly into the EKS cluster as DaemonSets. These components automatically collect:

Container logs

Pod metrics

Node metrics

Cluster events

OOM errors

CrashLoopBackOff restart cycles

Control plane request anomalies

With this data, the DevOps engineer can easily identify misconfigurations, resource bottlenecks, unhealthy nodes, failing containers, or image pull issues.

Option A (dashboards only) lacks per-container diagnostic data.

Option B (CloudTrail) only logs API calls — not useful for restart debugging.

Option C (CloudTrail Insights) only detects anomalous API usage, not container failures.

Therefore, CloudWatch Container Insights is the correct and AWS-recommended solution for diagnosing container restart failures in EKS.

Step 1: Handling Invalid Data with Failed Batch ItemsThe Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.

Action: Keep the Lambda function ' s batch size the same and configure it to report failed batch items.

Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

The requirement is to restrict production deployments strictly to the pipeline, while still allowing emergency access to a specific engineer.

The best approach is to restrict the DevOps team to read-only access in production accounts, minimizing risk of manual changes (Option A).

The DevOps engineer can have an admin permission set but assume the pipeline IAM role for deployment, enforcing strict control.

Applying an SCP to deny modification by anyone other than the pipeline role enforces this at the organization level. Option B is similar but unnecessarily creates separate IAM users, increasing management overhead. Option C grants the DevOps engineer broader permissions that may conflict with controls. Option D complicates management with tagging and SCPs, increasing operational overhead.

https://aws.amazon.com/about-aws/whats-new/2016/09/aws-codedeploy-introduces-deployment-monitoring-with-amazon-cloudwatch-alarms-and-automatic-deployment-rollback/

Option A directly matches all requirements with the least extra infrastructure:

State Manager is the Systems Manager capability for defining and maintaining a desired configuration by running associations on a schedule (for example, hourly). That satisfies the “run periodically” requirement in a managed way across a large fleet.

The AWS-ApplyChefRecipes Systems Manager document is specifically intended to run Chef recipes/cookbooks on managed instances without requiring you to run your own Chef server infrastructure . You can point it at a cookbook source (such as an artifact/repo location) and have Systems Manager handle execution on the instances.

Why the other options aren’t correct:

B is not the right mechanism for periodic Chef cookbook execution. Installing an application package and running a “repair action” via Run Command is not the purpose-built Chef cookbook runner, and it’s more brittle/DIY than State Manager associations.

C ( AWS-RefreshAssociation ) is used to refresh/reevaluate association metadata/targets; it does not itself execute Chef cookbooks as the compliance mechanism.

D is for OS patching compliance (Patch Manager/patch policies), not for applying Chef cookbooks that detect and remediate configuration drift.

 Configure AWS Systems Manager on Each Instance:

AWS Systems Manager provides a unified interface for managing AWS resources. Install the Systems Manager agent on each EC2 instance to enable inventory management and other features.

 Use AWS Systems Manager Inventory:

Systems Manager Inventory collects metadata about your instances and the software installed on them. This data includes information about applications, network configurations, and more.

Enable Systems Manager Inventory on all EC2 instances to gather detailed information about installed applications.

 Use Systems Manager Resource Data Sync to Synchronize and Store Findings in an Amazon S3 Bucket:

Resource Data Sync aggregates inventory data from multiple accounts and regions into a single S3 bucket, making it easier to query and analyze the data.

Configure Resource Data Sync to automatically transfer inventory data to an S3 bucket for centralized storage.

 Create an AWS Lambda Function that Runs When New Objects are Added to the S3 Bucket:

Use an S3 event to trigger a Lambda function whenever new inventory data is added to the S3 bucket.

The Lambda function can parse the inventory data and check for the presence of prohibited applications.

 Configure the Lambda Function to Identify Prohibited Applications:

The Lambda function should be programmed to scan the inventory data for any known prohibited applications and generate alerts or take appropriate actions if such applications are found.

Example Lambda function in Python

import json

import boto3

def lambda_handler(event, context):

s3 = boto3.client( ' s3 ' )

bucket = event[ ' Records ' ][0] [ ' s3 ' ][ ' bucket ' ][ ' name ' ]

key = event[ ' Records ' ][0] [ ' s3 ' ][ ' object ' ][ ' key ' ]

response = s3.get_object(Bucket=bucket, Key=key)

inventory_data = json.loads(response[ ' Body ' ].read().decode( ' utf-8 ' ))

prohibited_apps = [ ' app1 ' , ' app2 ' ]

for instance in inventory_data[ ' Instances ' ]:

for app in instance[ ' Applications ' ]:

if app[ ' Name ' ] in prohibited_apps:

# Send notification or take action

print(f " Prohibited application found: {app[ ' Name ' ]} on instance {instance[ ' InstanceId ' ]} " )

return { ' statusCode ' : 200, ' body ' : json.dumps( ' Check completed ' )}

By leveraging AWS Systems Manager Inventory, Resource Data Sync, and Lambda, this solution provides an efficient and automated way to audit EC2 instances for prohibited applications.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The correct answer is B because AWS Config can continuously evaluate the account-level S3 Block Public Access configuration by using a managed rule. When the configuration becomes noncompliant, AWS Config can trigger automatic remediation through an AWS Systems Manager Automation runbook to restore the required settings.

This solution directly satisfies both requirements:

Detect changes to account-level S3 public access settings.

Automatically revert those changes so that all public access is blocked again.

Why the other options are incorrect:

A. Security Hub is primarily for aggregated security findings and controls visibility. It is not the most direct or standard service for continuous configuration evaluation with automatic remediation of this specific setting.

C. An SCP can restrict permissions, but it does not automatically detect and revert changes to S3 Block Public Access settings.

D. Amazon Macie is for discovering and protecting sensitive data, not for continuously enforcing and remediating S3 Block Public Access account settings.

To analyze and find the number of logins for a specific user from the past 7 days, a CloudWatch Logs Insights query is the most suitable solution. CloudWatch Logs Insights enables you to interactively search and analyze your log data in Amazon CloudWatch Logs. You can use the query language to perform queries that contain multiple commands, including aggregation functions, which can count the occurrences of logins for a specific username over a specified time period. This approach is more direct and efficient than creating a metric filter or subscription, which would require additional steps to publish and sum a metric. References: AWS Certified DevOps Engineer - Professional, CloudWatch Logs Insights query syntax, Tutorial: Run a query with an aggregation function, Add or remove a number widget from a CloudWatch dashboard.

AWS CodePipeline supports multiple stages including source, build, test, and deploy. The most efficient way to integrate automated testing is by adding an AWS CodeBuild action to the pipeline that runs the tests using a buildspec.yml file. CodeBuild can be configured to fail the pipeline automatically if tests fail, ensuring that deployments do not proceed to the staging environment. This pattern is directly supported and documented in AWS CodePipeline + CodeBuild CI/CD architecture guidance.

C. Create new ALB and Auto Scaling group resources in the new Region and configure the new ALB to direct traffic to the new Auto Scaling group. This will allow users in the new Region to access the application with lower latency by reducing the network hops between the user and the application servers.

D. Create Amazon Route 53 records, health checks, and latency-based routing policies to route to the ALB. This will enable Route 53 to route user traffic to the nearest healthy ALB, based on the latency between the user and the ALBs.

F. Convert the DynamoDB table to a global table. This will enable reads and writes to the table in both Regions with low latency, improving the overall response time of the application

it involves adding an IPv6 CIDR block to the VPC and subnets for the ALB and specifying the dualstack IP address type on the ALB listener. This allows the ALB to listen on both IPv4 and IPv6 addresses, and forward requests to the EC2 instances that are added as targets to the target group associated with the ALB.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The requirement is to log and analyze Kubernetes control plane activity, including API requests, and to monitor container performance on the worker nodes with the least operational overhead.

Option B is correct because:

Amazon EKS control plane logging can send control plane logs, including API server logs, to Amazon CloudWatch.

CloudWatch Container Insights provides monitoring and performance visibility for containers, pods, nodes, and clusters.

CloudWatch Logs Insights can be used to query and analyze both control plane and node-related logs.

This is the most managed and lowest-overhead solution among the options.

Why the other options are incorrect:

A. CloudTrail is not the primary solution for Kubernetes control plane logs such as API server logs, and deploying Logstash increases operational overhead significantly.

C. Sending logs to Amazon S3 and analyzing them with Athena and QuickSight is more operationally complex and less direct than using native CloudWatch integrations.

D. AWS Distro for OpenTelemetry with Firehose and Redshift adds substantial complexity and operational overhead compared to native EKS and CloudWatch capabilities.

To scale microservice Pods based on CPU utilization, the Kubernetes Horizontal Pod Autoscaler (HPA) uses the Kubernetes Metrics Server to monitor resource usage and automatically adjusts the number of Pods. However, scaling Pods may require additional nodes if the current node capacity is insufficient.

The Cluster Autoscaler works with EKS managed node groups to add or remove worker nodes based on pending Pod requirements and resource usage.

By deploying both HPA and Cluster Autoscaler, the system can automatically scale Pods and add nodes as necessary, ensuring efficient resource utilization and availability.

Configuring the Cluster Autoscaler with auto-discovery allows it to manage node groups without manual intervention, reducing operational effort.

Option A only scales nodes based on node CPU utilization, not Pods.

Option B uses VPA recommender mode, which only suggests resource changes and does not scale automatically.

Option C involves manual updates and is not automated scaling. Therefore, option D provides the most operationally efficient, fully automated scaling solution.

Reference from AWS Official Documentation:

Kubernetes Horizontal Pod Autoscaler: " HPA automatically scales the number of Pods based on observed CPU utilization or other metrics. " (Kubernetes HPA)

Cluster Autoscaler on Amazon EKS: " The Cluster Autoscaler automatically adjusts the size of the Kubernetes cluster when there are Pods that fail to run due to insufficient resources or when nodes in the cluster are underutilized. " (AWS EKS Cluster Autoscaler)

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#LambdaFunctionExample

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

The requirement is simply: review changes before production deployment , with the least operational overhead .

B is the lightest change: adding a Manual approval (review) action in CodePipeline creates a controlled gate before the deploy stage. It requires no new repositories, no new services, and no custom code—just pipeline configuration.

Why not the others:

A introduces additional moving parts (Git repo integration, PR workflow management, and CloudFormation Git sync). That’s useful, but it’s more operational overhead than necessary to satisfy “review before deploy.”

C requires custom Lambda logic to inspect templates and decide whether to proceed—more code to write, run, secure, and maintain.

D adds both Git integration and a manual approval step—again more overhead than just adding the approval gate.

So B best meets the requirement with the least operational effort: a simple manual approval stage in the pipeline before production deployment.

ECR pull-through cache caches images from upstream repositories for resilience.

Private repo with basic scanning ensures vulnerability detection on pushed images.

Enabling pull-through caching on a private repo combines caching and vulnerability scanning seamlessly.

Public repos do not support pull-through caching of upstream images.

Replication rules are for multi-Region replication, not upstream caching.

The following are the steps that the DevOps engineer should take to ensure that all EBS volumes always have the Backup_Frequency tag so that the company can perform backups at least weekly unless a different value is specified:

Set up AWS Config in the account.

Use a managed rule that returns a compliance failure for EC2::Volume resources that do not have a Backup Frequency tag applied.

Configure a remediation action that uses a custom AWS Systems Manager Automation runbook to apply the Backup_Frequency tag with a value of weekly.

The managed rule AWS::Config::EBSVolumesWithoutBackupTag will return a compliance failure for any EBS volume that does not have the Backup_Frequency tag applied. The remediation action will then use the Systems Manager Automation runbook to apply the Backup_Frequency tag with a value of weekly to the EBS volume.

Amazon CloudWatch Logs now supports data protection policies that can mask sensitive data in logs at ingestion time using custom data identifiers. By creating a custom data identifier matching the employee ID pattern (Emp-\d{6}), the administrator can mask those patterns in new log entries automatically.

Assigning an IAM policy that denies the logs:Unmask action to engineers prevents them from seeing unmasked sensitive data, enforcing least privilege access.

This approach is operationally efficient because it uses built-in CloudWatch Logs data protection capabilities without the need for complex custom code or manual log filtering.

Option C requires writing and managing Lambda functions for log transformation, increasing operational overhead. Option D is more complex and reactive rather than preventing the sensitive data exposure. Option B incorrectly references NotAction and managed identifiers that may not match the custom pattern.

A bucket policy is a resource-based policy that defines who can access a specific S3 bucket and what actions they can perform on it. By removing unauthenticated access from the bucket policy, you can prevent anyone without valid credentials from accessing the bucket. A service role is an IAM role that allows an AWS service, such as CodeBuild, to perform actions on your behalf. By modifying the service role for the CodeBuild project to include Amazon S3 access, you can grant the project permission to read and write objects in the S3 bucket. The AWS CLI is a command-line tool that allows you to interact with AWS services, such as S3, using commands in your terminal. By using the AWS CLI to download the database population script, you can leverage the service role credentials and encryption to secure the data transfer.

For more information, you can refer to these web pages:

[Using bucket policies and user policies - Amazon Simple Storage Service]

[Create a service role for CodeBuild - AWS CodeBuild]

[AWS Command Line Interface]

https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-configurations.html

Option A is incorrect because EventBridge rules cannot filter events based on the message body or attributes of the target service. Therefore, configuring an SNS subscription filter policy to match the CloudFormation stack will not work. The SNS topic will receive all events from the EventBridge rule, regardless of the stack name or drift status.

Option B is incorrect because it introduces unnecessary complexity and cost. Creating a second Lambda function to query the CloudFormation API for the drift detection results is redundant, since CloudFormation already publishes drift detection events to EventBridge. Moreover, invoking two Lambda functions every hour will incur more charges than invoking one.

Option C is incorrect because GuardDuty does not provide drift detection for CloudFormation stacks. GuardDuty is a threat detection service that monitors for malicious activity and unauthorized behavior in AWS accounts and workloads. It does not monitor or report on configuration changes or drifts in CloudFormation stacks.

Option D is correct because it leverages AWS Config and its managed rule for drift detection. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. It can detect configuration changes and drifts in CloudFormation stacks using the cloudformation-stack-drift-detection-check managed rule. This rule triggers an AWS Config event when a stack drifts from its expected template configuration. By creating a second EventBridge rule that reacts to this event for the specific stack, the DevOps engineer can configure the SNS topic as a target and receive a notification as soon as possible when drift is detected.

 To meet the requirements, the DevOps engineer needs to create a solution that joins all EC2 instances to the domain of the AWS Managed Microsoft AD directory with the most operational efficiency. The DevOps engineer can use AWS Systems Manager Automation to automate the domain join process using an existing runbook called AWS-JoinDirectoryServiceDomain. This runbook can join Windows instances to an AWS Managed Microsoft AD or Simple AD directory by using PowerShell commands. The DevOps engineer can create an AWS::SSM::Association resource in the CloudFormation template to associate the runbook with the EC2 instances that have specific tags. The tags can be defined in the launch template and propagated on launch to the EC2 instances. The DevOps engineer can also define the required parameters for the runbook, such as the directory ID, directory name, and organizational unit. The DevOps engineer can attach the AmazonSSMManagedlnstanceCore and AmazonSSMDirectoryServiceAccess AWS managed policies to the IAM role that the EC2 instances use. These policies grant the necessary permissions for Systems Manager and Directory Service operations.

https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-logging-monitoring.html

The correct answer is C.

Option A is incorrect because using CloudWatch metrics to create a custom expression that identifies the CloudWatch log groups that have the most data being written to them is not a valid solution. CloudWatch metrics do not provide information about the size or volume of data being ingested by CloudWatch logs. CloudWatch metrics only provide information about the number of events, bytes, and errors that occur within a log group or stream. Moreover, creating a custom expression with CloudWatch metrics would require using the search_web tool, which is not necessary for this use case.

Option B is incorrect because using CloudWatch Logs Insights to create a set of queries for the application log groups to identify the number of logs written for a period of time is not a valid solution. CloudWatch Logs Insights can help analyze and filter log events based on patterns and expressions, but it does not provide information about the cost or billing of CloudWatch logs. CloudWatch Logs Insights also charges based on the amount of data scanned by each query, which could increase the logging costs further.

Option C is correct because using AWS Cost Explorer to generate a cost report that details the cost for CloudWatch usage is a valid solution. AWS Cost Explorer is a tool that helps visualize, understand, and manage AWS costs and usage over time. AWS Cost Explorer can generate custom reports that show the breakdown of costs by service, region, account, tag, or any other dimension. AWS Cost Explorer can also filter and group costs by usage type, which can help identify the specific CloudWatch log groups that are the source of the increased logging costs.

Option D is incorrect because using AWS CloudTrail to filter for CreateLogStream events for each application is not a valid solution. AWS CloudTrail is a service that records API calls and account activity for AWS services, including CloudWatch logs. However, AWS CloudTrail does not provide information about the cost or billing of CloudWatch logs. Filtering for CreateLogStream events would only show when a new log stream was created within a log group, but not how much data was ingested or stored by that log stream.

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The requirement is to shift traffic automatically in equal increments over a defined total deployment time with no manual intervention. In AWS CodeDeploy blue/green deployments for Amazon ECS, the TimeBasedLinear traffic routing option is specifically designed for this purpose.

Option A is correct because:

TimeBasedLinear shifts traffic in equal percentages.

The deployment proceeds automatically at each interval.

linearPercentage defines how much traffic is shifted each time.

linearInterval defines how often the traffic shift happens.

This supports gradual traffic shifting and zero-downtime blue/green deployment behavior.

Why the other options are incorrect:

B. AllAtOnce shifts all traffic immediately, not in equal increments over time.

C. TimeBasedCanary shifts traffic in an initial portion and then the remainder later. That is not equal incremental shifting across the full deployment period.

D. This option is too generic and does not specifically identify the CodeDeploy configuration that provides equal incremental automatic traffic shifting. The exact feature required is TimeBasedLinear.

https://aws.amazon.com/caching/session-management/

To meet the requirements of migrating its on-premises Windows and Linux applications to AWS and creating a pilot light DR environment in another AWS Region, the company should use Amazon FSx for NetApp ONTAP for the application storage. Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, high-performing, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports multiple protocols, including SMB for Windows and NFS for Linux, so the company can access the shared storage from both types of applications. FSx for ONTAP also supports NetApp SnapMirror replication, which enables the company to replicate the storage to the DR Region. NetApp SnapMirror replication is efficient, secure, and incremental, and it preserves the data deduplication and compression benefits of FSx for ONTAP. The company can use automation to launch and configure the EC2 instances in the DR Region and then use NetApp SnapMirror to restore the data from the primary Region.

The other options are not correct because they do not meet the requirements or follow best practices. Using Amazon S3 for the application storage is not a good option because S3 is an object storage service that does not support SMB or NFS protocols natively. The company would need to use additional services or software to mount S3 buckets as file systems, which would add complexity and cost. Using Amazon EBS for the application storage is also not a good option because EBS is a block storage service that does not support SMB or NFS protocols natively. The company would need to set up and manage file servers on EC2 instances to provide shared access to the EBS volumes, which would add overhead and maintenance. Using a Volume Gateway in AWS Storage Gateway for the application storage is not a valid option because Volume Gateway does not support SMB protocol. Volume Gateway only supports iSCSI protocol, which means that only Linux applications can access the shared storage.

1: What is Amazon FSx for NetApp ONTAP? - FSx for ONTAP

2: Amazon FSx for NetApp ONTAP

3: Amazon FSx for NetApp ONTAP | NetApp

4: AWS Announces General Availability of Amazon FSx for NetApp ONTAP

Replicating Data with NetApp SnapMirror - FSx for ONTAP

What Is Amazon S3? - Amazon Simple Storage Service

What Is Amazon Elastic Block Store (Amazon EBS)? - Amazon Elastic Compute Cloud

What Is AWS Storage Gateway? - AWS Storage Gateway

https://aws.amazon.com/blogs/mt/automate-remediation-actions-for-amazon-ec2-notifications-and-beyond-using-ec2-systems-manager-automation-and-aws-health/

 Create an Amazon EventBridge Rule Using an AWS CloudTrail Event Pattern:

AWS CloudTrail logs API calls made in your account, including actions performed by roles.

Create an EventBridge rule that matches CloudTrail events where the AssumeRole API call is made to assume the administrator role.

 Invoke an AWS Lambda Function:

Configure the EventBridge rule to trigger a Lambda function whenever the rule ' s conditions are met.

The Lambda function will handle the logic to send a notification.

 Publish a Message to an Amazon SNS Topic:

The Lambda function will publish a message to an SNS topic to notify the security team.

Subscribe the security team’s email address to this SNS topic to receive real-time notifications.

Example EventBridge rule pattern:

{

" source " : [ " aws.cloudtrail " ],

" detail-type " : [ " AWS API Call via CloudTrail " ],

" detail " : {

" eventSource " : [ " sts.amazonaws.com " ],

" eventName " : [ " AssumeRole " ],

" requestParameters " : {

" roleArn " : [ " arn:aws:iam:: < account-id > :role/AdministratorRole " ]

}

}

}

Example Lambda function (Node.js) to publish to SNS:

const AWS = require( ' aws-sdk ' );

const sns = new AWS.SNS();

exports.handler = async (event) = > {

const params = {

Message: `Administrator role assumed: ${JSON.stringify(event.detail)}`,

TopicArn: ' arn:aws:sns: < region > : < account-id > : < sns-topic > '

};

await sns.publish(params).promise();

};

The requirements describe a classic attribute-based access control (ABAC) model using AWS IAM. The company already has consistent tagging across users and resources, which is ideal for ABAC and least-privilege enforcement at scale.

First, Option A is required because IAM roles should represent job functions (developer vs. database administrator) and be scoped per project. Creating separate roles per project and job function allows permissions to be isolated cleanly and prevents cross-project access.

Second, Option B enforces project-level isolation at the policy level . By modifying the existing job-function policies to include a StringEquals condition that compares aws:ResourceTag/Project with aws:PrincipalTag/Project, access is automatically limited to only those resources that belong to the same project as the user. This is an AWS-recommended ABAC pattern and avoids policy sprawl.

Third, Option C is required so users can assume roles only when their tags match the role’s tags . An IAM policy attached to users that allows sts:AssumeRole only if both project and job function tags match ensures users cannot assume roles outside their assigned scope.

Options D, E, and F either misapply policies to the wrong entities, misuse tagging on policies (which IAM does not evaluate for authorization), or introduce unnecessary group-based management that conflicts with the ABAC design.

Therefore, A, B, and C together provide least privilege, project isolation, and scalable access control with minimal ongoing administration.

The most operationally efficient solution is to use AWS Systems Manager Parameter Store1 to store the AMI ID and reference it in the launch template2. This way, the launch template does not need to be updated every time a new AMI is created by Image Builder. Instead, the Image Builder pipeline can update the Parameter Store value with the newest AMI ID3, and the Auto Scaling group can launch instances using the latest value from Parameter Store.

The other solutions require updating the launch template or creating a new version of it every time a new AMI is created, which adds complexity and overhead. Additionally, using EventBridge rules and Lambda functions or Run Command documents introduces additional dependencies and potential points of failure.

 1: AWS Systems Manager Parameter Store 2: Using AWS Systems Manager parameters instead of AMI IDs in launch templates 3: Update an SSM parameter with Image Builder

https://aws.amazon.com/blogs/devops/how-to-test-and-debug-aws-codedeploy-locally-before-you-ship-your-code/#:~:text=You%20can%20test%20application%20code,local%20server%20or%20EC2%20instance.

Step 1: Using Service Control Policies (SCPs) for EC2 SecurityTo limit the use of EC2 instance credentials to the specific EC2 instance they are assigned to, you can create a Service Control Policy (SCP) that verifies specific conditions, such as whether the EC2 instance ' s source VPC and private IP match expected values.

Action: Create an SCP that checks whether the values of the aws:EC2InstanceSourceVPC and aws:SourceVpc condition keys are the same. Deny access if they are not.

Why: This ensures that credentials cannot be used outside the designated EC2 instance or VPC.

Step 2: Further Validation with Private IPsThe SCP should also verify that the EC2 instance ' s private IP matches the IP range specified for the VPC. If the instance ' s private IP does not match, access should be denied.

Action: In the same SCP, check whether the values of the aws:EC2InstanceSourcePrivateIP and aws:VpcSourceIP condition keys are the same. Deny access if they are not.

Why: This ensures that the credentials are only used within the specific EC2 instance and its associated VPC.

" The firewall appliance sends logs to Amazon CloudWatch Logs and includes event severities of CRITICAL, HIGH, MEDIUM, LOW, and INFO "

To archive CloudWatch Logs to S3 with long-term retention, you need:

a streaming mechanism to move data from CloudWatch Logs to S3, and

an S3 Lifecycle policy to handle tiering and expiration.

Option B uses a CloudWatch Logs subscription filter that targets Amazon Data Firehose . Firehose provides managed, scalable streaming from CloudWatch Logs to S3 with optional buffering and transformation. This is the AWS-recommended pattern for exporting continuous log streams with minimal operational overhead. Option C is not valid because CloudWatch Logs subscription filters cannot directly target S3; they must target Kinesis, Firehose, or Lambda.

Once logs are in S3, the company wants to keep them rarely accessed after 90 days and retained for 10 years. Option D configures an S3 lifecycle policy to transition logs to S3 Glacier Instant Retrieval after 90 days, which is a low-cost archival tier with relatively fast access, and to expire (delete) logs after 3,650 days (10 years). This precisely matches the retention requirement.

Option E uses Reduced Redundancy, which is a legacy storage class and not optimized for long-term archival. Therefore, the correct combination is B and D .

The CloudFormation IaC generator can reverse-engineer existing resources (partial scan) into infrastructure as code. Filtering by Lambda type creates CDK-ready constructs with minimal manual work. AWS recommends this method for IaC onboarding of existing workloads.

AWS CodeArtifact is a fully managed artifact repository service that supports popular package formats and can proxy downstream public repositories securely. It integrates seamlessly with both AWS Cloud and on-premises environments and stores artifacts in highly durable object storage (Amazon S3). CodeArtifact encrypts data at rest and in transit by default.

Because there is currently no routing between the on-premises data center and AWS, establishing a third-party software VPN appliance for connectivity provides secure communication without requiring complex AWS Direct Connect or multiple VPN setups.

This solution can be deployed quickly (within two weeks), is highly available, and meets encryption requirements. Using CodeArtifact eliminates the need to maintain third-party artifact servers, reducing operational overhead and improving reliability.

Options B, C, and D introduce higher complexity with AWS Direct Connect or multiple VPN connections and third-party software that increases deployment time and operational burden, which is against the requirement for rapid deployment and high operational efficiency.

Reference from AWS Official Documentation:

AWS CodeArtifact Overview: " CodeArtifact is a fully managed artifact repository service that makes it easy for organizations to securely store, publish, and share software packages used in their software development process. " (AWS CodeArtifact Documentation)

Encryption in AWS CodeArtifact: " CodeArtifact encrypts data at rest and in transit by default. " (AWS CodeArtifact Security)

VPN Appliance Deployment: " Software VPN appliances are commonly used for secure, encrypted connectivity between on-premises data centers and AWS. " (AWS VPN Options)

To achieve multi-Region high availability for a serverless API-based application, the architecture must be duplicated in the second Region and supported by a multi-Region data layer and appropriate DNS routing. Option A provides the required multi-Region data layer using DynamoDB global tables , which replicate data automatically and asynchronously between Regions. This allows reads and writes in either Region and prevents cross-Region latency bottlenecks.

Option C ensures that the compute components—API Gateway REST API and AWS Lambda functions—are deployed in both Regions. Each Region operates independently, providing true active-active redundancy and allowing Route 53 to route traffic directly to either Region without dependency on the other.

Option E selects Route 53 latency-based routing , which directs each user to the Region with the lowest round-trip latency. This meets the requirement to route traffic based on the Region providing the best response time rather than only providing active-passive failover.

Option D (failover routing) supports active-standby topologies, not active-active. Option B creates a global secondary index, which does not provide multi-Region replication. Option F (multivalue routing) distributes traffic randomly and does not account for latency or Region placement.

Thus, combining DynamoDB global tables, duplicated serverless architecture, and latency-based routing fully satisfies the requirements.

Amazon CloudWatch metric streams are designed to deliver metrics in near real time to downstream services like Amazon Data Firehose with minimal configuration and management overhead. By configuring a metric stream to include all metrics , you ensure that any newly created performance or custom metrics are automatically included without needing to update filters or code when namespaces or metrics change.

Option B uses a CloudWatch metric stream → Firehose → Lambda → S3 pattern. Firehose natively supports invoking a Lambda function for in-flight transformation of the metric data before final delivery to S3. This satisfies the requirement to transform metrics before storing them and provides a fully managed, serverless pipeline with very little ongoing operational burden.

Option A mentions including metrics from “the application and the CloudWatch namespace,” which implies more manual selection and maintenance, potentially missing new metrics if not explicitly added. Options C and D work at the log level, not at the metric level, and would require building additional logic to derive metrics from logs. This is more complex and not aligned with “least operational overhead.”

Therefore, Option B is the most efficient and scalable solution.

To meet the new guideline for application deployment, the company can use a combination of AWS CodePipeline and AWS CloudTrail. A manual approval action in CodePipeline allows the security team to review and approve changes before they are deployed. This action can be configured to pause the pipeline until approval is granted, ensuring that no changes move to production without the necessary sign-off. Additionally, by creating an AWS CloudTrail trail, all actions taken within CodePipeline, including approvals, are recorded and delivered to an Amazon S3 bucket. This provides an audit trail that can be retained for compliance and review purposes.

AWS CodePipeline’s manual approval action provides a way to ensure that a member of the security team can review and approve changes before they are deployed1.

AWS CloudTrail integration with CodePipeline allows for the recording and retention of all pipeline actions, including approvals, which can be stored in Amazon S3 for record-keeping2.

https://docs.aws.amazon.com/vpc/latest/userguide/managed-prefix-lists.html A managed prefix list is a set of one or more CIDR blocks. You can use prefix lists to make it easier to configure and maintain your security groups and route tables. https://docs.aws.amazon.com/vpc/latest/userguide/sharing-managed-prefix-lists.html With AWS Resource Access Manager (AWS RAM), the owner of a prefix list can share a prefix list with the following: Specific AWS accounts inside or outside of its organization in AWS Organizations An organizational unit inside its organization in AWS Organizations An entire organization in AWS Organizations

 The correct solution is to create a new assertion safety rule in Route 53 ARC and apply it to the two routing controls. An assertion safety rule is a type of safety rule that ensures that a minimum number of routing controls are always enabled. The ATLEAST type of assertion safety rule specifies the minimum number of routing controls that must be enabled for the rule to evaluate as healthy. By setting the threshold to 1, the rule ensures that at least one routing control is always turned on. This prevents the scenario where both routing controls are accidentally turned off and the application becomes unavailable in both Regions.

The other solutions are incorrect because they do not use safety rules to prevent both routing controls from being turned off. A gating safety rule is a type of safety rule that prevents routing control state changes that violate the rule logic. The OR type of gating safety rule specifies that one or more routing controls must be enabled for the rule to evaluate as healthy. However, this rule does not prevent a user from turning off both routing controls manually. A resource set is a collection of resources that are tested for readiness by Route 53 ARC. A readiness check is a test that verifies that all the resources in a resource set are operational. However, these concepts are not related to routing control states or safety rules. Therefore, creating a new resource set and a new readiness check will not ensure that at least one routing control is turned on at all times. References:

Routing control in Amazon Route 53 Application Recovery Controller

Viewing and updating routing control states in Route 53 ARC

Creating a control panel in Route 53 ARC

Creating safety rules in Route 53 ARC

The requirement is specifically to validate critical user journeys and API endpoints after each deployment and before routing traffic . The most direct AWS-native way to test “real” user flows and endpoints automatically is CloudWatch Synthetics canaries , which can run scripted checks (HTTP/API and browser-style journeys).

Option D minimizes operational overhead because it uses a straightforward managed pattern:

Canaries run against the new deployment to validate endpoints and journeys.

CloudWatch alarms trigger when the canaries fail (objective signal of broken journeys/APIs).

The solution then uses alarm-driven rollback (the intent here is automated rollback without writing custom analysis code or managing multi-service workflows).

Why the other options have more overhead or miss the “user journey/API validation” goal as cleanly:

A focuses on ECS task states (healthy/unhealthy tasks). That can detect deployment failures, but it doesn’t validate critical user journeys or API correctness . Also requires custom Lambda logic for analysis + rollback control.

B adds even more moving parts (Application Insights + alarms + EventBridge + Step Functions). Also, monitoring KPIs is not the same as explicitly validating journeys/endpoints before routing traffic .

C is the highest overhead: canaries plus X-Ray instrumentation across all microservices, trace exporting, custom Lambda analysis, etc. That’s heavy compared to canary + alarm.

So D best matches: “validate journeys/endpoints” + “automated detect + rollback” with least operational overhead by relying on managed canaries and alarms rather than custom code/workflows.

https://docs.aws.amazon.com/glue/latest/dg/components-overview.html

You need to understand how SCP inheritance works in AWS. The way it works for Deny policies is different that allow policies.

Allow polices are passing down to children ONLY if they don ' t have an allow policy.

Deny policies always pass down to children.

That ' s why there is always an SCP set to the Root to allow everything by default. If you limit this policy, the whole organization will be limited, not matter what other policies are saying for the other OUs. So it ' s not A. It ' s not D because it restricts the wrong OU.

 Impact of Removing FullAWSAccess and Adding Policy for EC2 Actions:

The FullAWSAccess policy allows all actions on all resources by default. Removing this policy from the Department OU will limit the permissions that accounts within this OU inherit from the parent OU.

Adding a policy that allows only Amazon EC2 API operations will restrict the permissions to EC2 actions only.

 Permissions of Administrative IAM Roles:

The administrative IAM roles in the Engineering OU have the AdministratorAccess policy attached, which grants full access to all AWS services and resources.

Since SCPs are restrictions that apply at the organizational level, removing FullAWSAccess and replacing it with a policy allowing only EC2 actions means that for all accounts in the Engineering OU:

They will have full access to EC2 actions due to the new SCP.

They will be restricted in other actions that are not covered by the SCP, hence, non-EC2 API actions will be denied.

 Conclusion:

All API actions on EC2 resources will be allowed.

All other API actions will be denied due to the absence of a broader allow policy.

 Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:

The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.

 Use AWS CodeBuild as the Action Provider to Run Unit Tests:

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.

Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.

Example configuration in CodePipeline:

{

" name " : " BuildStage " ,

" actions " : [

{

" name " : " Build " ,

" actionTypeId " : {

" category " : " Build " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 1

},

{

" name " : " Test " ,

" actionTypeId " : {

" category " : " Test " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 2

}

]

}

By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.

Step 1: Reducing Replication Lag in Aurora Global DatabasesIn Amazon Aurora global databases, write operations on the primary cluster can be delayed due to the time it takes to replicate to secondary clusters, especially when there are multiple secondary regions involved.

Issue: The write operations are occasionally blocked due to the RPO setting, which guarantees replication within 60 seconds.

Action: Remove one of the secondary clusters from the global database.

Why: Fewer secondary clusters will reduce the overall replication lag, improving write performance and reducing the frequency of blocked writes.

 Step 1: Using AWS IAM Identity Center for SAML-based Identity Federation

To ensure that all users accessing the AWS Management Console are authenticated via the corporate identity provider (IdP), the best approach is to set up identity federation with AWS IAM Identity Center (formerly AWS SSO) using SAML 2.0.

Action: Use AWS IAM Identity Center to configure identity federation with the corporate IdP that supports SAML 2.0.

Why: SAML 2.0 integration enables single sign-on (SSO) for users, allowing them to authenticate through the corporate IdP and gain access to AWS resources.

Step 1: Creating an Identity Provider in IAMYou first need to configure AWS to trust the external identity provider (IdP), which in this case supports OpenID Connect (OIDC). The IdP will handle the authentication, and AWS will handle the authorization based on the IdP ' s token.

Action: Create an IAM Identity Provider (IdP) in AWS using the existing provider ' s URL, audience, and signature. This step is essential for establishing trust between AWS and the external IdP.

Why: This allows AWS to accept tokens from your external IdP (auth.company.com) for authentication.

The requirement is to prevent any product files containing unannounced product IDs (prefixed with a specific UUID) from being stored in the production S3 bucket that users can access.

To achieve this, a best practice is to use a staging bucket as a control point before files go to production, combined with Amazon Macie’s data classification capabilities.

Creating a custom data identifier in Amazon Macie allows precise detection of product IDs starting with the specific UUID, which default managed identifiers will not detect.

By running a Macie sensitive data discovery job on the staging bucket, you can identify files containing these sensitive product IDs.

Only files without findings (i.e., files that do not contain unannounced product IDs) are copied to the production bucket, ensuring no sensitive information is exposed. This approach aligns with AWS best practices for data classification and staged deployment workflows, maximizing control and reducing risk. Using Macie on the production bucket directly (options B and D) risks exposing sensitive data before detection and deletion. Option C uses managed data identifiers, which will likely not detect the custom UUID prefix pattern.

Reference from AWS Official Documentation and Study Guide:

Amazon Macie Custom Data Identifiers: " You can create custom data identifiers in Amazon Macie to find sensitive data that is unique to your organization. " (Amazon Macie User Guide)

Data Security Best Practices: " Use staging environments to inspect and sanitize data before moving it to production to reduce exposure risks. " (AWS Security Best Practices)

(A) Use ECR replication to keep images synchronized between Regions, minimizing CI/CD pipeline changes.

(C) DynamoDB global tables allow multi-Region replication and provide local read/write access, so tasks should interact with the DynamoDB replica in their Region.

(D) Use S3 cross-Region replication or separate buckets with replication; tasks access the bucket in the same Region for latency and data sovereignty.

(B) Using DynamoDB global table but pointing tasks to only one Region reduces resilience.

(E) S3 Multi-Region Access Points are newer but add complexity and are not required for minimal changes.

(F) Managing CI/CD pipeline for multiple Regions and failover routing adds complexity beyond minimal changes.

AWS Secrets Manager is a secrets management service that helps you protect access to your applications, services, and IT resources. This service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. Using Secrets Manager, you can secure and manage secrets used to access resources in the AWS Cloud, on third-party services, and on-premises. SSM parameter store and AWS Secret manager are both a secure option. However, Secrets manager is more flexible and has more options like password generation. Reference: https://www.1strategy.com/blog/2019/02/28/aws-parameter-store-vs-aws-secrets-manager/

Failed interactive logins such as ConsoleLogin are part of CloudTrail management events , not data events. The most direct, low-overhead design is to stream management events to CloudWatch Logs , then create a metric filter that matches failed ConsoleLogin events (for example, $.eventName = " ConsoleLogin " and $.responseElements.ConsoleLogin = " Failure " or $.errorMessage ). That metric is then used as the basis for a CloudWatch alarm . When the alarm threshold (e.g., N failures in a period) is breached, the alarm triggers and sends a notification to the already created SNS topic, which alerts the security team.

Option A follows this recommended pattern: CloudTrail → CloudWatch Logs → Metric Filter → Alarm → SNS. It is fully managed, near real-time, and requires minimal custom logic.

Option B uses Athena with EventBridge to periodically query CloudTrail logs in S3. This introduces more moving parts, more configuration, higher latency, and more operational overhead.

Options C and D incorrectly reference CloudTrail data events and S3 event notifications, which are not the right mechanisms to detect ConsoleLogin failures. Console logins are not S3 events, and using data events would miss these management actions.

Therefore, Option A is the correct and simplest solution.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/install-CloudWatch-Agent-on-premise.html https://docs.aws.amazon.com/xray/latest/devguide/xray-api-sendingdata.html

Activating S3 server access logging and using Amazon Athena to create an external table with the log files is the easiest and most cost-effective way to analyze access patterns. This option requires minimal setup and allows for quick analysis of the access patterns with SQL queries. Additionally, Amazon Athena scales automatically to match the query load, so there is no need for additional infrastructure provisioning or management.

https://docs.aws.amazon.com/config/latest/developerguide/ec2-instance-profile-attached.html

 Step 1: Centralizing Image Scanning in a Shared Services Account

The first requirement is to centralize the image scanning process, ensuring pre-scan and post-scan images are stored separately. This can be achieved by creating separate pre-scan and post-scan repositories in the shared services account, with the appropriate resource-based policies to control access.

Action: Create separate ECR repositories for pre-scan and post-scan images in the shared services account. Configure resource-based policies to allow write access to pre-scan repositories and read access to post-scan repositories.

Why: This ensures that images are isolated before and after the scan, following the compliance requirements.

The following are the steps involved in accomplishing this in the most maintainable manner:

Use AWS CodeBuild with artifact encryption to replace the Jenkins instance running on EC2 instances.

Configure CodeBuild to encrypt the build artifacts using AWS Secrets Manager.

Deploy the containerized quality control applications to CodeBuild.

This approach is the most maintainable because it eliminates the need to manage Jenkins on EC2 instances. CodeBuild is a managed service, so the DevOps engineer does not need to worry about patching or upgrading the service.

https://docs.aws.amazon.com/codebuild/latest/userguide/security-encryption.html Build artifact encryption - CodeBuild requires access to an AWS KMS CMK in order to encrypt its build output artifacts. By default, CodeBuild uses an AWS Key Management Service CMK for Amazon S3 in your AWS account. If you do not want to use this CMK, you must create and configure a customer-managed CMK. For more information Creating keys.

Using the principalTag in the Permission Set inline policy a logged in user belonging to a specific AD group in the IDP can be permitted access to perform operations on certain resources if their group matches the group used in the PrincipleTag. Basically you are narrowing the scope of privileges assigned via Permission policies conditionally based on whether the logged in user belongs to a specific AD Group in IDP. The mapping of the AD group to the request attributes can be done using SSO attributes where we can pass other attributes like the SAML token as well.

https://docs.aws.amazon.com/singlesignon/latest/userguide/abac.html

https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/automating-updates-to-serverless-apps.html

Only the Organizations management account (or roles in that account) can call organizations:CreateAccount directly. When moving the Lambda function to a different (dedicated) account, the correct pattern is to perform cross-account role assumption into a role that resides in the management account and has tightly scoped Organizations permissions.

Option A describes exactly this:

Create an IAM role in the management account with only the required Organizations permissions (for example, organizations:CreateAccount and related read permissions).

Configure the trust policy on that role to allow it to be assumed by the Lambda execution role in the new account.

Update the Lambda code to call sts:AssumeRole into the management-account role before invoking the Organizations API.

This approach ensures that:

The Lambda function can create new accounts, but only via the management account.

The Lambda execution role in the dedicated account has no direct Organizations permissions; it only has permission to assume the specific role.

Option B is incorrect because “delegated administration for Organizations” in the generic sense is not how CreateAccount is exposed; account creation remains a management-account responsibility. Options C and D either misconfigure trust or introduce unnecessary Control Tower complexity. Cross-account assume-role from the dedicated account into the management account is the correct and least-privilege solution.