The answer is to enable Dataproc autoscaling and configure memory-based scaling. This is correct because memory-based scaling directly addresses the root cause of the out-of-memory (OOM) failure by monitoring YARN memory utilization across the cluster and dynamically adding worker nodes when memory pressure rises, ensuring the Spark job has enough heap space to complete without manual intervention. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataproc’s adaptive scaling policies versus static sizing or retry-based fixes; a common trap is choosing a larger fixed machine type (Option A), which wastes cost and still risks OOM if load varies, or splitting the job (Option C), which doesn’t solve the underlying memory shortage. Memory tip: think “scale on pressure, not on guess”—memory-based scaling reacts to real-time usage, unlike CPU-based or schedule-based autoscaling, making it the only option that prevents OOM automatically while keeping costs efficient.
PDE Designing data processing systems Practice Question
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 data engineer is responsible for a batch ETL pipeline that runs daily using Cloud Composer and Dataproc. The pipeline extracts data from Cloud SQL, transforms it with Spark, and loads to BigQuery. Last night, the pipeline failed because the Spark job ran out of memory. The team needs a solution that prevents future failures without manual intervention. Options: A. Use a larger machine type for Dataproc. B. Enable Dataproc autoscaling and configure memory-based scaling. C. Split the Spark job into multiple stages. D. Use Cloud Functions to retry the job.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Dataproc autoscaling and configure memory-based scaling
Option A is correct because Dataproc autoscaling with memory-based scaling dynamically adjusts the cluster size based on the memory utilization of running jobs. This prevents out-of-memory failures by automatically adding worker nodes when memory pressure increases, without requiring manual intervention or pre-provisioning oversized clusters. It directly addresses the root cause—insufficient memory during peak processing—while maintaining cost efficiency.
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.
✓
Enable Dataproc autoscaling and configure memory-based scaling
Why this is correct
Autoscaling adjusts cluster size based on memory usage, preventing OOM.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Cloud Functions to retry the job
Why it's wrong here
Retrying does not fix the underlying memory issue.
✗
Use a larger machine type for Dataproc
Why it's wrong here
Static sizing may still fail with variable data volumes.
✗
Split the Spark job into multiple stages
Why it's wrong here
Splitting may help with complexity but not necessarily memory exhaustion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that retrying a failed job or manually resizing resources is a sufficient solution, when in fact dynamic, automated scaling is required to handle variable workloads without manual intervention.
Detailed technical explanation
How to think about this question
Dataproc autoscaling uses a YARN-based metrics collector that monitors memory and CPU utilization across the cluster. When memory usage exceeds a configurable threshold (e.g., 80%), it triggers scale-out by adding preemptible or standard worker nodes, while scale-in occurs when utilization drops below a lower threshold. This is particularly effective for Spark jobs with variable data volumes, such as daily ETL pipelines, where memory demand can spike unpredictably due to data skew or shuffle operations.
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: Enable Dataproc autoscaling and configure memory-based scaling — Option A is correct because Dataproc autoscaling with memory-based scaling dynamically adjusts the cluster size based on the memory utilization of running jobs. This prevents out-of-memory failures by automatically adding worker nodes when memory pressure increases, without requiring manual intervention or pre-provisioning oversized clusters. It directly addresses the root cause—insufficient memory during peak processing—while maintaining cost efficiency.
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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