Quick Answer
The correct answer is to delete the cluster after the job completes and to use preemptible VMs. These two actions directly reduce Dataproc cost because preemptible VMs, which Compute Engine can terminate at any time, offer a steep discount over standard instances, making them ideal for fault-tolerant, stateless nightly batch jobs. Deleting the cluster when idle eliminates ongoing compute charges for unused resources, a core best practice for ephemeral workloads. On the Google Professional Data Engineer exam, this scenario tests your understanding of cost optimization for transient, fault-tolerant pipelines—a common trap is keeping a cluster alive for convenience, which incurs unnecessary costs. Remember the memory tip: “Kill and chill”—kill the cluster after the job, and chill with preemptibles for the heavy lifting.
This PDE practice question tests your understanding of building and operationalizing 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.
Which TWO actions can reduce the cost of running a Dataproc cluster for a nightly batch job?
Increase the number of worker nodes for faster processing.
Why wrong: More nodes increase cost.
Use high-memory machine types for master node.
Why wrong: High-memory is more expensive.
Use preemptible VMs for worker nodes.
Preemptible VMs are much cheaper.
Attach local SSDs to all nodes.
Why wrong: Local SSDs increase cost.
Delete the cluster after the job completes.
Deleting avoids paying for idle time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use preemptible VMs for worker nodes.
Preemptible VMs (Option C) are significantly cheaper than standard VMs because Compute Engine can terminate them at any time, making them ideal for fault-tolerant, stateless batch jobs like nightly data processing on Dataproc. Deleting the cluster after the job completes (Option E) eliminates ongoing compute costs for idle resources, which is a best practice for ephemeral workloads.
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.
Increase the number of worker nodes for faster processing.
Why it's wrong here
More nodes increase cost.
Use high-memory machine types for master node.
Why it's wrong here
High-memory is more expensive.
Use preemptible VMs for worker nodes.
Why this is correct
Preemptible VMs are much cheaper.
Related concept
Read the scenario before looking for a memorised answer.
Attach local SSDs to all nodes.
Why it's wrong here
Local SSDs increase cost.
Delete the cluster after the job completes.
Why this is correct
Deleting avoids paying for idle time.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that scaling up resources (more nodes or faster hardware) always reduces cost by shortening runtime, but in reality, the increased per-hour cost usually outweighs the time savings for batch jobs.
Detailed technical explanation
Preemptible VMs in Dataproc are charged at a much lower rate (typically about 60-80% less than standard VMs) but can be preempted within 30 seconds of a termination notice. Dataproc automatically handles preemption by restarting failed tasks on remaining workers, making this cost-saving measure safe for batch jobs that can tolerate interruptions. Deleting the cluster stops all VM instances and associated persistent disks, ensuring no further compute charges accrue, while job history and logs can be retained in Cloud Storage or Stackdriver.
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.
What to study next
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FAQ
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Use preemptible VMs for worker nodes. — Preemptible VMs (Option C) are significantly cheaper than standard VMs because Compute Engine can terminate them at any time, making them ideal for fault-tolerant, stateless batch jobs like nightly data processing on Dataproc. Deleting the cluster after the job completes (Option E) eliminates ongoing compute costs for idle resources, which is a best practice for ephemeral workloads.
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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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data pipeline uses Cloud Composer (Airflow) to orchestrate Dataproc jobs. Each job submits a Spark application that reads from BigQuery and writes to Cloud Storage. The pipeline runs nightly and takes 6 hours. Management wants to reduce costs. Which approach is most effective?
mediumWhy A: Preemptible VMs are significantly cheaper (up to 80% discount) than standard VMs and are ideal for fault-tolerant, batch workloads like nightly Dataproc jobs. Since the pipeline runs nightly and takes 6 hours, it can tolerate the occasional preemption of worker nodes by using Spark's built-in resilience (e.g., task retries). This directly reduces compute cost without sacrificing completion, assuming the cluster is configured with enough preemptible workers to handle the workload.
Last reviewed: Jun 30, 2026
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