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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A data engineering team keeps the Python script for an AWS Glue ETL job in an AWS CodeCommit repository. The team wants every commit to automatically: 1. package the script, 2. update a development Glue job, 3. pause for manager approval, and 4. promote the change to the production Glue job. Which approach delivers this CI/CD workflow with the least custom code and operational overhead?

  • Add an S3 trigger to both Glue job script locations that invokes a Lambda function; the function pulls the latest commit from CodeCommit and updates the jobs without any intermediate steps.

  • Use AWS CodeDeploy to create deployment groups for the Glue job and set up a deployment pipeline that pushes the script to development and production, inserting a wait step before the production deployment.

  • Create an AWS CodePipeline with a CodeCommit source stage, a CodeBuild stage that packages the script to Amazon S3, a CloudFormation deploy action for the development Glue job, a manual approval action, and a second CloudFormation deploy action for the production Glue job.

  • Configure an Amazon EventBridge rule to start an AWS Glue workflow that copies the latest script to both development and production jobs, then ask engineers to manually trigger the production job after testing.

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