hardMultiple ChoiceObjective-mapped
PDE Practice Question: A company runs a batch data processing workload…
A company runs a batch data processing workload using Dataproc clusters that are auto-scaled based on YARN memory utilization. During peak times, jobs take much longer than expected. Analysis shows the cluster is not scaling up despite high YARN memory utilization. What is the most likely cause?
⚠ Common exam trap
Many exam-takers assume autoscaling applies to all worker nodes equally, overlooking the Dataproc-specific distinction between primary and secondary workers and the autoscaler's limitation to secondary workers only.
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
✓
The cluster is using primary workers only; auto-scaling only adds secondary workers
Dataproc clusters have two types of workers: primary workers (which run both HDFS and compute) and secondary workers (compute-only). The autoscaler can only add or remove secondary workers; it cannot scale primary workers. If the cluster uses only primary workers, the autoscaler has no secondary workers to add, so it cannot scale up even under high YARN memory utilization. This explains why the cluster remains static during peak times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Spark dynamic allocation is disabled, preventing executors from using added workers
Why it's wrong here
Spark dynamic allocation affects executor lifecycle, not cluster scaling.
- ✗
The cluster autoscaler is misconfigured to scale based on CPU, not memory
Why it's wrong here
Autoscaler uses YARN memory utilization by default; this would still trigger scale-up.
- ✗
The autoscaler is set to scale down secondary workers, not up
Why it's wrong here
Autoscaler can scale up and down; this does not explain lack of scale-up.
- ✓
The cluster is using primary workers only; auto-scaling only adds secondary workers
Why this is correct
Auto-scaling adds secondary workers, not primary; if only primary workers exist, no scale-up occurs.
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