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

Option C is correct because the company only needs to keep the original files for 90 days and then delete them. AWS states that S3 Lifecycle can be used to delete expired objects automatically, which directly matches this requirement. There is no need to transition the objects to another storage class because the files are not needed beyond day 90. Adding a transition step would add complexity and, in this case, would not improve cost-effectiveness.

Option A is not optimal because S3 Glacier Flexible Retrieval has a 90-day minimum storage duration and is intended for longer-term archival. Transitioning objects right before deleting them is unnecessary overhead and can add minimum-duration charges. Option B is incorrect because Object Lock is for retention and write-once-read-many protection, not for normal temporary retention with lowest cost. Option D is also less cost-effective because S3 Intelligent-Tiering is useful when access patterns are uncertain, but here the retention period and deletion timing are already known. The simplest and cheapest solution is to keep the originals in S3 Standard for the required 90 days and then expire them with Lifecycle.

 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

Option B is the most operationally efficient because it checks the business requirement directly: whether the target table contains the most recent data, not merely whether a job failed. Monitoring only failures (Options C and D) can produce false positives (a job failure might not impact freshness) and false negatives (a job can succeed but still load stale or incomplete data). The study material emphasizes implementing data quality validation as part of the ETL process so data can be verified before or as it is stored, rather than relying only on pipeline execution status.

Using a data quality rule focused on freshness (for example, validating that a “max event timestamp” or “latest partition date” meets today’s expected value) lets the pipeline detect stale loads even when the workflow runs. Then, an EventBridge rule can route failures of that data quality check to SNS for immediate notification, keeping operations serverless and centralized. Macie (Option A) is designed for sensitive-data discovery/classification, not operational “freshness” checks on Redshift tables, so it adds unnecessary services and effort compared to a Glue-native data quality validation approach.