Data-Engineer-Associate Dumps with Practice Exam Questions Answers

 This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer’s IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

Troubleshoot Errors - Amazon SageMaker

AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess

In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.

IAM Roles for Service Accounts (IRSA):

With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.

This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift

The DatasetMatch rule in DQDL checks for full value equivalence between mapped fields. A value of 1.0 indicates a 100% match. The correct syntax and metric for an exact match scenario are:

“Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Options with “100” use incorrect syntax since DQDL uses floating-point scores (e.g., 1.0, 0.95), not percentages.