The answer is to increase the persistent disk size on each worker node to 100 GB. This directly resolves the Dataproc disk quota exceeded error because the 10 GB disks are too small to hold intermediate shuffle data, which PySpark writes to local storage during wide transformations like joins or aggregations. As data volume grows, the shuffle spill exceeds the disk quota, causing job failures. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataproc’s ephemeral storage architecture and the distinction between compute resources (vCPUs, memory) and disk resources for shuffle operations. A common trap is to upgrade the machine type or add local SSDs, which is more expensive and unnecessary; simply increasing the persistent disk size is the cost-effective fix that scales with data growth. Memory tip: “Shuffle spills need disk thrills”—when you see disk quota errors in Dataproc, think bigger persistent disks, not bigger machines.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing data processing systems. 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 healthcare analytics company runs a nightly Dataproc workflow that reads radiology reports from Cloud Storage (CSV files), transforms them using PySpark, and writes results to BigQuery. The workflow is orchestrated by Cloud Composer. Recently, the job has started failing with 'Disk quota exceeded' errors on the worker nodes. The data volume has grown 5x over the past month. Currently, the cluster uses 5 n1-standard-4 workers (each 10GB persistent disk). The PySpark jobs heavily use intermediate shuffles. You need a cost-effective solution that avoids future failures as data grows. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the persistent disk size on each worker node to 100 GB.
The 'Disk quota exceeded' error occurs because the 10 GB persistent disks on the n1-standard-4 workers are too small to accommodate the intermediate shuffle data, which has grown 5x. Increasing the persistent disk size to 100 GB directly addresses the storage bottleneck without changing the machine type or incurring the cost of local SSDs, making it a cost-effective solution that scales with data growth.
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.
✗
Upgrade the worker machine type to n1-standard-8 with local SSDs for shuffle storage.
Why it's wrong here
Local SSDs are ephemeral and cannot be used for intermediate shuffle data; also more expensive.
✓
Increase the persistent disk size on each worker node to 100 GB.
Why this is correct
More disk space per worker allows shuffles to complete without quota errors.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add more preemptible workers to the cluster and keep boot disk size at 10GB.
Why it's wrong here
Adding workers increases total disk but each worker still has only 10GB; shuffle data is stored locally on the workers doing the shuffle.
✗
Use Cloud Dataflow instead of Dataproc, as it handles disk management transparently.
Why it's wrong here
Migration to Dataflow is a significant effort and may not directly address disk quota issues.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may over-engineer the solution by upgrading machine types or switching to a different service (Dataflow) when the root cause is simply insufficient disk space for shuffle data, which is easily fixed by increasing the persistent disk size.
Detailed technical explanation
How to think about this question
In Dataproc, shuffle data is stored on the worker's persistent disk by default (in the /tmp or /hadoop/yarn/local directories). When intermediate shuffle data exceeds the available disk space, the job fails with 'Disk quota exceeded'. Increasing persistent disk size is the simplest fix; note that you can also configure `spark.shuffle.service.enabled` or use `spark.local.dir` to point to attached SSDs, but for cost-effectiveness, resizing the boot disk is preferred. Real-world scenarios often see this when data volumes grow unpredictably, and resizing disks is a standard operational practice.
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.
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the persistent disk size on each worker node to 100 GB. — The 'Disk quota exceeded' error occurs because the 10 GB persistent disks on the n1-standard-4 workers are too small to accommodate the intermediate shuffle data, which has grown 5x. Increasing the persistent disk size to 100 GB directly addresses the storage bottleneck without changing the machine type or incurring the cost of local SSDs, making it a cost-effective solution that scales with data growth.
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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