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PDE Practice Question: Migrating on-premises Apache Spark jobs to Google…
A company is migrating on-premises Apache Spark jobs to Google Cloud Dataproc. They want to reduce operational overhead and minimize costs. Which architecture is most appropriate?
⚠ Common exam trap
It's easy for candidates to choose Cloud Dataproc Serverless (Option A) thinking it eliminates all operational overhead, but they overlook that it lacks the cost-saving benefits of preemptible VMs and may not support all Spark features, making auto-scaling clusters with preemptible VMs the more appropriate choice for minimizing costs in a migration scenario.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Dataproc clusters with auto-scaling and preemptible VMs.
Dataproc clusters with auto-scaling and preemptible VMs directly address the need to reduce operational overhead and minimize costs for on-premises Spark migrations. Auto-scaling dynamically adjusts cluster size based on workload, while preemptible VMs (which cost 60-80% less than standard VMs) handle fault-tolerant tasks, making this the most cost-effective and operationally efficient architecture for Spark on Dataproc.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Dataproc Serverless for all Spark jobs.
Why it's wrong here
Serverless may not support custom Spark configurations.
- ✗
Migrate jobs to Cloud Dataflow.
Why it's wrong here
Dataflow is not Spark-compatible.
- ✗
Run Spark on Compute Engine instances with startup scripts.
Why it's wrong here
Requires manual cluster management.
- ✓
Use Dataproc clusters with auto-scaling and preemptible VMs.
Why this is correct
Reduces cost and operational overhead.
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