The answer is to increase the persistent disk size per worker to 100 GB. This resolves the error because Dataflow’s shuffle operation uses local disk as scratch space for intermediate data; when the disk is too small, high I/O contention slows the shuffle, causing workers to miss the 30-second data sample window. On the Google Professional Data Engineer exam, this scenario tests your understanding that shuffle disk I/O bottlenecks are often misdiagnosed as compute or networking issues—a common trap is to keep adding workers or upgrading machine types, which doesn’t fix the underlying disk throughput limit. Remember that for batch pipelines processing hundreds of gigabytes, the default 25 GB persistent disk per worker is insufficient for shuffle-heavy workloads; increasing it to 100 GB provides more I/O bandwidth and local storage. A useful memory tip: “Shuffle needs space, not just pace”—disk size, not worker count, solves slow shuffle I/O.
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 financial services company runs a batch Dataflow pipeline daily to process transaction data. The pipeline reads from Cloud Storage, performs complex transformations, and writes to BigQuery. Recently, the pipeline has been failing intermittently with the error: 'Workflow failed. Causes: (9c3f7a2b1d4e): The worker missed 2000 data samples in the last 30 seconds. This can be caused by a variety of factors, including slow work items, network issues, or resource contention.' The team has already increased the number of workers and tried using e2-standard-8 machine types, but the issue persists. The pipeline processes approximately 500 GB of data per run and uses approximately 200 workers. The team suspects that the issue might be related to shuffle operations. What should the team do next to resolve the issue?
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 per worker to 100 GB.
The error indicates that workers are missing data samples due to slow shuffle operations, often caused by insufficient disk I/O. Increasing the persistent disk size per worker to 100 GB provides more local scratch space for Dataflow's shuffle, reducing disk contention and allowing the shuffle to complete within the 30-second window. This directly addresses the root cause without changing the worker count or machine type.
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 Streaming Engine for the pipeline.
Why it's wrong here
Streaming Engine is for streaming pipelines, not batch.
✓
Increase the persistent disk size per worker to 100 GB.
Why this is correct
Provides more space for shuffle data, reducing disk contention.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of workers to 100 to decrease shuffle overhead.
Why it's wrong here
Fewer workers may increase per-worker load and worsen the issue.
✗
Use Cloud Storage as a shuffle sink.
Why it's wrong here
Dataflow does not support Cloud Storage as a shuffle sink; shuffle is disk-based.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing workers or machine type always solves performance issues, when in fact shuffle-bound pipelines require adequate local disk I/O, not just more CPU or memory.
Detailed technical explanation
How to think about this question
Dataflow's shuffle operation uses local persistent disk as scratch space; when the disk is too small, the shuffle spills to disk more frequently, causing I/O wait and worker slowdowns. The 30-second timeout for data samples is a Dataflow health check; if a worker cannot process samples fast enough due to disk thrashing, it is considered unhealthy. In practice, increasing persistent disk size to at least 100 GB (or using SSD persistent disk) provides sufficient throughput for shuffle-heavy batch pipelines processing hundreds of GB.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 per worker to 100 GB. — The error indicates that workers are missing data samples due to slow shuffle operations, often caused by insufficient disk I/O. Increasing the persistent disk size per worker to 100 GB provides more local scratch space for Dataflow's shuffle, reducing disk contention and allowing the shuffle to complete within the 30-second window. This directly addresses the root cause without changing the worker count or machine type.
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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