Quick Answer
The answer is to increase the number of worker nodes to 20 and use preemptible VMs for half of them. This is correct because CPU-bound Spark jobs scale nearly linearly with additional cores, so doubling the worker count from 10 to 20 directly halves runtime, while substituting half the nodes with preemptible VMs achieves the most cost-effective solution by leveraging their significantly lower price. On the Google Professional Data Engineer exam, this scenario tests your understanding of dataproc cost optimization preemptible vms, specifically how to balance performance and budget for ephemeral, fault-tolerant workloads. A common trap is assuming you must avoid preemptible VMs entirely due to their short lifespan, but Spark’s resilience and the nightly batch nature of the job make them ideal here. Memory tip: for CPU-bound jobs, think “more cores, less cost” — double the workers, halve the price with preemptibles.
This PDE practice question tests your understanding of designing data processing systems. 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 uses Cloud Dataproc to run nightly Spark ETL jobs that process about 500 GB of data each night. The jobs currently take 4 hours to complete. The company wants to reduce the runtime to under 2 hours to meet a new SLA. The cluster is configured with 10 worker nodes (n1-standard-4) and 1 master node (n1-standard-4). The jobs are CPU-bound and use only default settings. The cluster is deleted after each job and recreated. The data is stored in Cloud Storage. The company is open to increasing cost but wants the most cost-effective solution to meet the SLA. Which approach should they take?
Use a regional Cloud Storage bucket to improve read throughput.
Why wrong: Not CPU-bound.
Replace worker nodes with n1-highmem-16 instances to increase memory.
Why wrong: Memory is not the bottleneck.
Increase the number of worker nodes to 20 and use preemptible VMs for half of them.
Doubles processing power cost-effectively.
Change machine type to n2-standard-8 for all nodes.
Why wrong: May be more expensive per core.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the number of worker nodes to 20 and use preemptible VMs for half of them.
Option C is correct because adding more worker nodes (from 10 to 20) directly increases parallelism for CPU-bound Spark jobs, and using preemptible VMs for half of them reduces cost while still meeting the SLA. Since the job is CPU-bound and uses default settings, scaling horizontally with a mix of standard and preemptible VMs is the most cost-effective way to halve runtime, as Spark can efficiently distribute the workload across more cores.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use a regional Cloud Storage bucket to improve read throughput.
Why it's wrong here
Not CPU-bound.
Replace worker nodes with n1-highmem-16 instances to increase memory.
Why it's wrong here
Memory is not the bottleneck.
Increase the number of worker nodes to 20 and use preemptible VMs for half of them.
Why this is correct
Doubles processing power cost-effectively.
Related concept
Read the scenario before looking for a memorised answer.
Change machine type to n2-standard-8 for all nodes.
Why it's wrong here
May be more expensive per core.
Common exam traps
The trap here is that candidates may assume CPU-bound jobs require faster CPUs (Option D) or more memory (Option B), but horizontal scaling with preemptible VMs is the most cost-effective way to increase parallelism in Cloud Dataproc.
Detailed technical explanation
Spark's default parallelism is often tied to the number of cores available; increasing worker nodes from 10 to 20 doubles the number of task slots, allowing more partitions to be processed concurrently. Preemptible VMs in Cloud Dataproc cost about 60-80% less than standard VMs but can be terminated at any time; for nightly batch jobs that can tolerate occasional failures, using them for half the workers reduces cost while maintaining performance, as Spark's speculative execution can re-run lost tasks on remaining nodes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the number of worker nodes to 20 and use preemptible VMs for half of them. — Option C is correct because adding more worker nodes (from 10 to 20) directly increases parallelism for CPU-bound Spark jobs, and using preemptible VMs for half of them reduces cost while still meeting the SLA. Since the job is CPU-bound and uses default settings, scaling horizontally with a mix of standard and preemptible VMs is the most cost-effective way to halve runtime, as Spark can efficiently distribute the workload across more cores.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Variation 1. 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?
mediumWhy C: 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.
Last reviewed: Jun 11, 2026
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