PDE Maintaining and Automating Data Workloads Practice Question
You are running a Dataproc cluster for batch processing. The job is not latency-sensitive and you want to minimize cost. You notice that the cluster is underutilized during the job. Which configuration change would reduce costs most effectively?
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 preemptible workers for the worker nodes
Using preemptible workers in Dataproc reduces cost by about 60-80% compared to standard VMs. They are suitable for fault-tolerant batch jobs because they can be terminated at any time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Resize the cluster to use larger machines
Why it's wrong here
Larger machines increase cost, not reduce.
- ✗
Switch to single-node cluster
Why it's wrong here
Single-node may not provide enough resources and doesn't address underutilization across workers.
- ✓
Use preemptible workers for the worker nodes
Why this is correct
Preemptible workers significantly reduce costs and are ideal for batch jobs that can tolerate interruptions.
- ✗
Enable autoscaling
Why it's wrong here
Autoscaling adjusts the number of workers but still uses standard VMs; preemptible workers are cheaper.
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