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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A data engineering team stores AWS Glue PySpark scripts in an AWS CodeCommit repository. An AWS CodePipeline currently builds the artifacts and deploys them through AWS CloudFormation. The team must add an automated test stage that installs Python dependencies, executes pytest-based unit tests that import AWS Glue 4.0 libraries, and fails the pipeline if any test fails, while minimizing cost and infrastructure management. Which solution meets these requirements?

  • Insert an AWS Batch job action that spins up an EMR on EKS cluster during the pipeline run to execute the PySpark unit tests and then terminates the cluster.

  • Introduce a manual approval action that requires a data engineer to execute the tests on a dedicated EC2 instance before continuing the pipeline.

  • Add an AWS CodeBuild stage to the pipeline that uses the public AWS Glue 4.0 Docker image as its build environment, runs pip install -r requirements.txt followed by pytest, and lets the build's exit code determine pipeline success.

  • Add an AWS Glue job configured with job bookmarks in "Test run" mode; configure CodePipeline to monitor a CloudWatch metric from the job and fail if the metric reports errors.

AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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