You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of…

Question

Options

• AMove your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
• BRewrite your jobs in Apache Beam. Run your jobs in Dataflow.
• CCopy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
• DMove your data to Cloud Storage. Run your jobs on Dataproc.

• A
  4% (1)
• B
  17% (4)
• C
  9% (2)
• D
  70% (16)

Explanation

D is correct because Dataproc is Google Cloud's managed Spark/Hadoop service - it runs existing Spark jobs with minimal code changes, and pairing it with Cloud Storage (instead of HDFS) is the standard lift-and-shift migration path that satisfies both the "managed service" and "minimal code changes" requirements.

A is wrong because converting Spark scripts to SQL for BigQuery is a significant rewrite, violating the "minimum code changes" constraint. B is wrong because rewriting in Apache Beam/Dataflow is also a substantial code overhaul, not a quick migration. C is wrong because self-managing Spark on Compute Engine VMs is essentially recreating an unmanaged cluster - the opposite of using a managed service.

Memory tip: Think of Dataproc as "Hadoop/Spark as a service" - if the question mentions existing Spark or Hadoop jobs and asks for minimal changes, Dataproc + Cloud Storage is almost always the answer. Dataflow is for new pipelines written in Beam, while BigQuery is for SQL analytics, not Spark job migration.

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