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PDE Practice Question: A data engineer is designing a batch processing…
A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to confuse HDFS with Cloud Storage, assuming intermediate data must be stored locally for performance, but Cloud Storage is actually faster and cheaper for transient data in Dataproc due to its native integration and lack of replication overhead.
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
✓
Set autoscaling policies based on YARN memory.
Autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Always use persistent disks for all nodes.
Why it's wrong here
Preemptible VMs have local SSDs.
- ✓
Set autoscaling policies based on YARN memory.
Why this is correct
Optimizes resource utilization.
- ✗
Store intermediate data in HDFS.
Why it's wrong here
Use Cloud Storage instead.
- ✓
Use preemptible VMs for worker nodes.
Why this is correct
Reduces cost significantly.
- ✗
Use the largest machine types for master nodes.
Why it's wrong here
Over-provisioning increases cost.
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