Quick Answer
The answer is to use preemptible VMs for worker nodes and configure autoscaling based on YARN memory. Preemptible VMs drastically reduce compute costs for fault-tolerant batch workloads, as Dataproc can seamlessly recover interrupted tasks, while autoscaling based on YARN memory ensures the cluster dynamically adjusts worker nodes to match real-time resource demand, preventing both over-provisioning and job slowdowns. On the Google Professional Data Engineer exam, this pairing tests your understanding of cost-performance tradeoffs in ephemeral clusters—a common trap is choosing static scaling or ignoring preemptible VMs for non-critical nodes. Remember that YARN memory is the key metric Dataproc uses to trigger scaling actions, not CPU or disk I/O. A helpful memory tip: “Preempt for price, YARN memory for yield”—preemptible VMs cut costs, and YARN-based autoscaling optimizes throughput.
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 engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)
Always use persistent disks for all nodes.
Why wrong: Preemptible VMs have local SSDs.
Set autoscaling policies based on YARN memory.
Optimizes resource utilization.
Store intermediate data in HDFS.
Why wrong: Use Cloud Storage instead.
Use preemptible VMs for worker nodes.
Reduces cost significantly.
Use the largest machine types for master nodes.
Why wrong: Over-provisioning increases cost.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Set autoscaling policies based on YARN memory.
Option B is correct because autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.
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.
Always use persistent disks for all nodes.
Why it's wrong here
Preemptible VMs have local SSDs.
Set autoscaling policies based on YARN memory.
Why this is correct
Optimizes resource utilization.
Related concept
Read the scenario before looking for a memorised answer.
Store intermediate data in HDFS.
Why it's wrong here
Use Cloud Storage instead.
Use preemptible VMs for worker nodes.
Why this is correct
Reduces cost significantly.
Related concept
Read the scenario before looking for a memorised answer.
Use the largest machine types for master nodes.
Why it's wrong here
Over-provisioning increases cost.
Common exam traps
The trap here is that candidates often confuse HDFS with Cloud Storage, assuming intermediate data must be stored locally for performance, but Cloud Storage is actually faster and cheaper for transient data in Dataproc due to its native integration and lack of replication overhead.
Detailed technical explanation
Under the hood, Cloud Dataproc's autoscaler uses the YARN ResourceManager's metrics (e.g., pending memory, available memory) to compute a target number of worker nodes. It scales up when pending memory exceeds a threshold and scales down after a cooldown period to avoid thrashing. A real-world scenario: a batch job with variable data volume can trigger scale-up during shuffle phases and scale-down during idle periods, saving costs without manual intervention.
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
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Set autoscaling policies based on YARN memory. — Option B is correct because autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.
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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Last reviewed: Jun 11, 2026
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.
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