Crucial Exams

GCP Professional Data Engineer Practice Question

A retailer runs a 50-node on-prem Hadoop cluster that supports ad-hoc Hive queries for data analysts during the day and Spark ETL jobs overnight. Management wants a fast lift-and-shift to Google Cloud that keeps the existing Hive metastore and avoids rewriting job submission scripts. They accept some idle cost in the short term but want the option to manually scale down workers during quiet periods. Which Dataproc deployment approach best satisfies these requirements while modernization is planned?

  • Rewrite the Hive queries as BigQuery SQL and migrate all data directly into BigQuery to eliminate cluster management entirely.

  • Create a single persistent Dataproc cluster sized similarly to the on-prem environment, store data in Cloud Storage, and manually resize the cluster when workload volumes change.

  • Use Dataproc Serverless for Spark, submitting every batch and interactive job through the serverless API instead of managing clusters.

  • Refactor each Spark and Hive workload to launch its own ephemeral Dataproc cluster that is created at job start and deleted on completion.

GCP Professional Data Engineer
Maintaining and automating data workloads
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