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GCP Professional Data Engineer Practice Question

Your team must automate a nightly data pipeline that 1) launches a parameterized Dataflow Flex template, 2) executes a BigQuery stored procedure on completion, and 3) calls an external Jira REST endpoint to update a ticket. The run order must change if the Dataflow job fails, and business analysts want to reuse existing Python libraries for custom retry logic. All resources will run in a private VPC with no public IPs. Which orchestration approach best meets these requirements?

  • Chain Cloud Functions with Pub/Sub topics: the first function launches the Dataflow template, a second polls Dataflow and invokes the BigQuery procedure, and a third updates Jira.

  • Schedule the Dataflow Flex template with Cloud Scheduler, embed BigQuery execution and Jira calls inside the pipeline, and rely on Dataflow's built-in retries for error handling.

  • Use Cloud Workflows triggered nightly by Cloud Scheduler; call Dataflow, BigQuery, and Jira REST endpoints with HTTP steps and handle failures with conditional states.

  • Create a private-IP Cloud Composer environment and build a Python DAG that uses built-in Dataflow and BigQuery operators plus a PythonOperator that posts to the Jira REST API.

GCP Professional Data Engineer
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