Question 969 of 1,000
PDE Ingesting and Processing the Data Practice Question
This PDE practice question tests your understanding of ingesting and processing the data. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company needs to run a Spark ML training job on a Dataproc cluster with high memory per node, but the cluster should automatically scale down when idle to save costs. Which configuration should they use?
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 a Dataproc cluster with preemptible secondary workers and cluster autoscaling
Option D is correct because it combines preemptible secondary workers for cost-effective high-memory compute with cluster autoscaling, which automatically scales down the cluster when idle. Preemptible VMs are ideal for stateless Spark ML training tasks, and autoscaling ensures the cluster shrinks to save costs during inactivity. This configuration meets the requirement of high memory per node (via primary workers) while minimizing costs through idle scaling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception is that preemptible VMs can be used as primary workers or that autoscaling works with preemptible primary workers, but in Dataproc, preemptible VMs are restricted to secondary workers to maintain cluster stability.
Detailed technical explanation
How to think about this question
Dataproc autoscaling uses the YARN Memory-based or Spark Dynamic Allocation to adjust cluster size based on pending tasks, scaling down to zero secondary workers when idle. Preemptible secondary workers are ideal for Spark ML training because they handle transient, fault-tolerant workloads and cost about 60-80% less than regular VMs, but they can be terminated at any time, so they must not host critical state. The cluster's primary workers (non-preemptible) handle persistent data like HDFS, while secondary workers provide elastic compute capacity.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Questions learners often ask
What does this PDE question test?
Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a Dataproc cluster with preemptible secondary workers and cluster autoscaling — Option D is correct because it combines preemptible secondary workers for cost-effective high-memory compute with cluster autoscaling, which automatically scales down the cluster when idle. Preemptible VMs are ideal for stateless Spark ML training tasks, and autoscaling ensures the cluster shrinks to save costs during inactivity. This configuration meets the requirement of high memory per node (via primary workers) while minimizing costs through idle scaling.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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