hardMultiple ChoiceObjective-mapped
PDE Practice Question: A company runs a batch processing job on Dataproc…
A company runs a batch processing job on Dataproc that uses Apache Spark to process 500 GB of data daily. The job completes successfully but takes 4 hours. The team wants to reduce the runtime to under 2 hours without increasing cost. What should they do?
⚠ Common exam trap
Google Cloud often tests the trade-off between cost and performance by making candidates think that upgrading machine types (more CPU/memory) is the only way to speed up a job, ignoring that preemptible VMs allow scaling out (more nodes) without increasing cost.
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 VMs for worker nodes and increase the number of workers.
Preemptible VMs cost significantly less than standard VMs (about 60-80% discount). By using preemptible VMs for worker nodes, you can increase the number of workers (and thus parallelism) without increasing cost. This directly reduces runtime by distributing the 500 GB workload across more executors, while the cost savings from preemptible VMs offset the additional nodes.
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 preemptible VMs for worker nodes and increase the number of workers.
Why this is correct
Preemptible VMs are cheaper, allowing more workers for the same cost, reducing runtime.
- ✗
Increase the master node's machine type to n2-standard-8.
Why it's wrong here
Master node size has minimal impact on processing speed.
- ✗
Increase the machine type of worker nodes to n2-highmem-8.
Why it's wrong here
Higher CPU machines cost more, potentially increasing cost.
- ✗
Migrate the job to Dataflow with autoscaling enabled.
Why it's wrong here
Migration may require code changes and Dataflow costs may not be lower.
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