- A
Enable Dataproc autoscaling and configure memory-based scaling
Autoscaling adjusts cluster size based on memory usage, preventing OOM.
- B
Use Cloud Functions to retry the job
Why wrong: Retrying does not fix the underlying memory issue.
- C
Use a larger machine type for Dataproc
Why wrong: Static sizing may still fail with variable data volumes.
- D
Split the Spark job into multiple stages
Why wrong: Splitting may help with complexity but not necessarily memory exhaustion.
Question 772 of 1,000
mediumMultiple ChoiceObjective-mapped
Dataproc Autoscaling: Memory-Based Scaling for Batch ETL
This PDE practice question tests your understanding of pde exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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. Enable Dataproc autoscaling and configure memory-based scaling. B. Use Cloud Functions to retry the job. C. Use a larger machine type for Dataproc. D. Split the Spark job into multiple stages.
Quick Answer
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.
Answer choices
Why each option matters
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 using a larger machine type for the Dataproc cluster directly addresses the memory insufficiency that caused the Spark job to fail. By selecting a machine type with more memory, the cluster has sufficient resources to handle the peak load of the transformation step. This is a simple, reliable solution that does not require manual intervention after initial configuration. While autoscaling could also work, it adds complexity and might not guarantee sufficient memory if the workload spikes quickly. Splitting the job may help but requires redesign, and retrying alone does not solve the resource issue.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.
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FAQ
Questions learners often ask
What does this PDE question test?
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 using a larger machine type for the Dataproc cluster directly addresses the memory insufficiency that caused the Spark job to fail. By selecting a machine type with more memory, the cluster has sufficient resources to handle the peak load of the transformation step. This is a simple, reliable solution that does not require manual intervention after initial configuration. While autoscaling could also work, it adds complexity and might not guarantee sufficient memory if the workload spikes quickly. Splitting the job may help but requires redesign, and retrying alone does not solve the resource issue.
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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Last reviewed: Jun 30, 2026
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