The answer is to add a secondary worker group using preemptible VMs and increase the number of workers. This is the most cost-effective way to improve Cloud Dataproc performance because preemptible VMs offer up to an 80% discount over regular instances, allowing you to horizontally scale out processing capacity for peak-hour batch ETL pipelines without redesigning the pipeline. Cloud Dataproc automatically distributes work across these additional workers, directly addressing the bottleneck of longer job durations when reading from Cloud Storage, transforming data, and writing to BigQuery. On the Google Professional Data Engineer exam, this scenario tests your understanding of cost optimization and cluster scaling strategies—a common trap is choosing to upgrade to higher-memory master nodes or redesign the pipeline, which wastes time and money. Remember the memory tip: “Preempt for peak, persist for base”—use preemptible VMs in a secondary worker group to handle temporary load spikes cheaply, while primary workers handle steady-state tasks.
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 runs a batch ETL pipeline on Cloud Dataproc. During peak hours, the job takes longer than expected. The pipeline reads from Cloud Storage, transforms data, and writes to BigQuery. What is the most cost-effective way to improve performance without redesigning the pipeline?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add a secondary worker group using preemptible VMs and increase the number of workers.
Adding a secondary worker group with preemptible VMs is the most cost-effective way to improve performance because it allows you to scale out the cluster horizontally with compute instances that are significantly cheaper (up to 80% discount) than regular VMs. This directly addresses the bottleneck of processing capacity during peak hours without requiring any pipeline redesign, as Cloud Dataproc can automatically distribute work across additional workers.
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.
✓
Add a secondary worker group using preemptible VMs and increase the number of workers.
Why this is correct
Preemptible VMs are cost-effective and add parallelism.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable local SSDs on all worker nodes.
Why it's wrong here
SSDs increase cost, and the bottleneck is likely CPU or memory.
✗
Increase the master node's machine type to n1-highmem-32.
Why it's wrong here
Master node size does not affect worker parallelism.
✗
Use Cloud Composer to schedule the job with a higher priority.
Why it's wrong here
Composer does not improve runtime of the job itself.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume scaling up the master node or improving local storage will help, but the exam tests understanding that horizontal scaling with cheap, ephemeral workers is the most cost-effective approach for batch processing workloads that are CPU-bound and fault-tolerant.
Detailed technical explanation
How to think about this question
Preemptible VMs in Cloud Dataproc are Compute Engine instances that last up to 24 hours and can be terminated at any time, but they are ideal for batch ETL jobs that are fault-tolerant and can handle interruptions. Cloud Dataproc automatically handles worker preemption by redistributing tasks to remaining nodes, and using a secondary worker group ensures that preemptible instances are not used for HDFS storage, so data durability is maintained. In practice, you can often achieve 2-3x performance improvement at 20-30% of the cost of using only standard VMs, making this a classic cost-performance trade-off scenario.
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: Add a secondary worker group using preemptible VMs and increase the number of workers. — Adding a secondary worker group with preemptible VMs is the most cost-effective way to improve performance because it allows you to scale out the cluster horizontally with compute instances that are significantly cheaper (up to 80% discount) than regular VMs. This directly addresses the bottleneck of processing capacity during peak hours without requiring any pipeline redesign, as Cloud Dataproc can automatically distribute work across additional workers.
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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