Crucial Exams

GCP Professional Data Engineer Practice Question

Your team maintains several Apache Beam pipelines packaged as Dataflow Flex Templates together with Cloud Composer DAGs that invoke those templates. You must introduce CI/CD so that any change to pipeline code or DAG definitions is version-controlled, traced, tested, and then automatically deployed to development, staging, and production projects. Operations effort must stay minimal and only managed Google Cloud services may be used. Which approach should you recommend?

  • Keep the Flex Template specification files inside each environment's Composer bucket with object versioning enabled and rely on Airflow's GitSync sidecar to propagate changes.

  • Commit pipeline code to a private GitHub repository and manually run Cloud Composer's Import DAG feature after every merge, triggering Dataflow jobs through a Cloud Function webhook.

  • Publish compiled pipeline JARs and DAG Python files directly to Artifact Registry and schedule a Cloud Scheduler job that invokes gcloud builds submit weekly to redeploy them.

  • Store Beam source and DAG files in Cloud Source Repositories and create Cloud Build triggers that run tests, build the Flex Template container, push it to Artifact Registry, and copy updated DAGs to each Cloud Composer environment's bucket.

GCP Professional Data Engineer
Ingesting and processing the data
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