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GCP Professional Data Engineer Practice Question

A retail analytics division runs nightly Spark ETL pipelines for four business units. All jobs currently share a single persistent Dataproc cluster backed by Cloud Storage. During month-end closes, long-running joins from one unit starve executors needed by others, and the central platform team spends hours tuning YARN queues. Management asks you to eliminate cross-team resource contention, keep costs low when jobs are idle, and avoid complex capacity management. What should you do?

  • Launch an ephemeral Dataproc cluster for each team's nightly job, run the Spark pipeline, and configure the cluster to delete itself when the job succeeds or fails.

  • Enable autoscaling on the persistent cluster and add preemptible secondary workers to handle month-end peaks.

  • Migrate the Spark pipelines to BigQuery using BigQuery-Connector and execute them as scheduled queries with on-demand pricing.

  • Resize the existing persistent cluster to a larger machine class and define stricter YARN capacity scheduler queues for each business unit.

GCP Professional Data Engineer
Maintaining and automating data workloads
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