Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

S3 Lifecycle policies automate data retention and deletion. By specifying an expiration rule for 7 years, objects older than that period are permanently deleted without manual intervention.

“To automatically delete aged data, configure an S3 Lifecycle rule with an expiration policy for objects older than the retention period.”

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

Option B is correct because Amazon Q in QuickSight is the AWS feature built specifically for natural language interaction with business data. AWS documentation explains that Amazon Q in QuickSight lets users ask questions in natural language, generate summaries and data stories, and create visuals from author prompts. AWS Prescriptive Guidance also notes that users can generate insights with natural language prompts and that they do not have to write SQL queries or learn complex BI tooling. That matches the requirement that some analysts lack SQL knowledge and need to create reports frequently with minimal effort.

Option A is not the best answer because zero-ETL access addresses data movement and integration, not natural-language dashboard creation. Option C introduces another analytics platform and therefore more operational effort. Option D provides access to data, but federated querying still does not address the key requirement for natural-language dashboard and report generation for nontechnical analysts. The AWS-native feature that directly solves this problem with the least additional management is Amazon Q in QuickSight.

 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