The answer is using a combination of standard and preemptible VMs for worker nodes to reduce Dataproc cost. This works because preemptible VMs are up to 80% cheaper than standard instances, and Spark’s built-in fault tolerance—via speculative execution and data shuffling to Cloud Storage—handles node preemptions without sacrificing performance. On the Google Professional Data Engineer exam, this scenario tests your understanding of ephemeral cluster architecture and cost optimization, often appearing as a trap where candidates mistakenly choose to reduce master node SKUs or lower cluster size, which would degrade stability or throughput. The key insight is that master nodes must remain standard for cluster reliability, while preemptible workers absorb the bulk of processing. Memory tip: think “Master stays, workers play—preemptibles save the day.”
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 uses Cloud Dataproc to run Spark jobs on ephemeral clusters. The input data is in Cloud Storage and output is also to Cloud Storage. The cluster is created and deleted daily. The cost is high due to spinning up nodes. Which change can reduce cost without sacrificing performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a combination of standard and preemptible VMs for worker nodes
Option C is correct because using a combination of standard and preemptible VMs for worker nodes reduces cost significantly while maintaining performance. Preemptible VMs are up to 80% cheaper than standard VMs, and since Spark is fault-tolerant and can handle node preemptions via speculative execution, the job can complete without performance degradation. Standard VMs for master nodes ensure cluster stability, while preemptible workers handle the bulk of data processing.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 standard VMs with a larger number of smaller machines
Why it's wrong here
More smaller machines may increase communication overhead and not reduce cost proportionally.
✗
Use Cloud Dataflow instead
Why it's wrong here
Switching to Dataflow may not be feasible for Spark-specific code; the question asks for a change to the existing setup.
✓
Use a combination of standard and preemptible VMs for worker nodes
Why this is correct
Preemptible VMs for workers reduce cost significantly; standard VMs for the master and a few worker nodes ensure reliability.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use preemptible VMs for all nodes
Why it's wrong here
All preemptible can cause job failures if VMs are reclaimed; master should be standard for reliability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that preemptible VMs can be used for all nodes, but the trap here is that the master node must be a standard VM to avoid cluster instability, while workers can safely use preemptible VMs due to Spark's fault tolerance.
Detailed technical explanation
How to think about this question
Preemptible VMs in Google Cloud have a maximum lifetime of 24 hours and can be terminated at any time with a 30-second notice. Spark's speculative execution, enabled by default in Dataproc, re-runs tasks on other nodes when a node is preempted, ensuring job completion. In practice, using 50-80% preemptible workers is common for cost-sensitive batch jobs, as the job completion time increases only marginally due to occasional task re-execution.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a combination of standard and preemptible VMs for worker nodes — Option C is correct because using a combination of standard and preemptible VMs for worker nodes reduces cost significantly while maintaining performance. Preemptible VMs are up to 80% cheaper than standard VMs, and since Spark is fault-tolerant and can handle node preemptions via speculative execution, the job can complete without performance degradation. Standard VMs for master nodes ensure cluster stability, while preemptible workers handle the bulk of data processing.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.