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 data team uses Cloud Dataproc to run nightly Spark jobs. The job volume has increased, and the cluster is often underutilized during the day. They want to reduce costs while ensuring jobs can scale when needed. Which strategy should they adopt?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a cluster with a small number of primary workers and a large pool of preemptible workers, and enable autoscaling.
Option C is correct because it combines a small number of primary (non-preemptible) workers for reliability with a large pool of preemptible workers for cost-effective scaling, and enables autoscaling to dynamically adjust the cluster size based on workload. This minimizes cost during idle periods (preemptible instances are ~80% cheaper) while ensuring jobs can scale up quickly when needed, as autoscaling adds preemptible workers automatically. Preemptible workers are ideal for fault-tolerant Spark jobs that can handle node preemptions.
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 preemptible workers for both primary and secondary nodes to minimize cost.
Why it's wrong here
Preemptible nodes can terminate at any time, risking data loss if used for HDFS.
✗
Manually scale the cluster up before nightly jobs and down after.
Why it's wrong here
Manual scaling is not efficient and doesn't handle unexpected spikes.
✓
Use a cluster with a small number of primary workers and a large pool of preemptible workers, and enable autoscaling.
Why this is correct
Preemptible workers are cheap, and autoscaling adjusts to load.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use custom machine types with local SSDs for primary workers to improve I/O.
Why it's wrong here
Custom machines increase cost and don't address underutilization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that preemptible instances can be used for all nodes, but the trap here is that primary nodes require non-preemptible instances for cluster stability, while preemptible workers are only suitable for secondary (task) nodes in a fault-tolerant framework.
Detailed technical explanation
How to think about this question
Cloud Dataproc autoscaling uses a secondary worker group that can be configured with preemptible instances; the autoscaler monitors YARN memory and CPU metrics to add or remove preemptible workers based on a target utilization (default 0.9). Preemptible instances have a maximum lifespan of 24 hours and can be reclaimed at any time, so Spark jobs must be designed with checkpointing or speculative execution to handle interruptions. In practice, a common pattern is to set a minimum number of primary workers for cluster coordination and let autoscaling handle the ephemeral compute for data processing tasks.
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 cluster with a small number of primary workers and a large pool of preemptible workers, and enable autoscaling. — Option C is correct because it combines a small number of primary (non-preemptible) workers for reliability with a large pool of preemptible workers for cost-effective scaling, and enables autoscaling to dynamically adjust the cluster size based on workload. This minimizes cost during idle periods (preemptible instances are ~80% cheaper) while ensuring jobs can scale up quickly when needed, as autoscaling adds preemptible workers automatically. Preemptible workers are ideal for fault-tolerant Spark jobs that can handle node preemptions.
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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Question Discussion
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