Crucial Exams

GCP Professional Data Engineer Practice Question

Your analytics team keeps a 40-node Dataproc cluster running 24×7 to execute a 2-hour Spark ETL that starts at 00:00 and to host interactive PySpark notebooks during business hours. Monitoring shows that over 80 % of cluster CPU is idle across a week. Management asks you to cut compute cost without increasing the notebook users' startup latency. What should you do?

  • Attach an autoscaling policy to the current cluster and set its worker count range to 0-40 so the cluster scales down to zero after the ETL finishes.

  • Convert the existing cluster's secondary workers to preemptible VMs and continue running notebooks and the nightly ETL on the same cluster.

  • Keep a small persistent Dataproc cluster for notebooks and launch the nightly ETL on an ephemeral job-scoped cluster that deletes itself when the job completes.

  • Move the ETL to a scheduled BigQuery query and keep the 40-node Dataproc cluster for interactive notebooks.

GCP Professional Data Engineer
Maintaining and automating data workloads
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