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GCP Professional Data Engineer Practice Question

Your data engineering team operates a 50-node Dataproc cluster that runs a nightly Spark ETL job on clickstream data for about two hours. The rest of the day the cluster is largely idle, except for occasional ad-hoc Hive queries from analysts who can wait a few minutes for results to start returning. Management asks you to lower compute costs while keeping existing SLAs. What approach should you take?

  • Retire Dataproc and rewrite the Spark pipelines as BigQuery SQL, using only on-demand query pricing.

  • Keep the existing persistent cluster but attach an autoscaling policy so all workers can scale down to zero when idle.

  • Run each ETL and ad-hoc workload on an ephemeral Dataproc cluster that is created when the job is submitted and deleted when it completes.

  • Keep the persistent cluster and convert all worker nodes to preemptible VMs while leaving master nodes unchanged.

GCP Professional Data Engineer
Maintaining and automating data workloads
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