- A
Increase the number of workers to distribute the load.
Why wrong: Scaling out may alleviate CPU per worker but doesn't reduce total CPU; also, autoscaling should handle this. The issue is likely inefficient processing.
- B
Change the trigger to event time with a 10-minute allowed lateness.
Why wrong: Event time triggers don't reduce computational load; they change when results are emitted.
- C
Replace GroupByKey with Combine.globally and use a fanout.
Combine.globally with fanout reduces the number of unique keys tracked per worker, lowering CPU usage from grouping large numbers of keys.
- D
Use side inputs to broadcast a static lookup table to all workers.
Why wrong: Side inputs add memory overhead and are not a generic solution for CPU-bound transformations.
Quick Answer
The answer is to replace GroupByKey with Combine.globally and use a fanout to reduce CPU load in a Dataflow streaming job. This works because Combine.globally performs partial aggregation on each worker before shuffling, dramatically cutting network I/O and CPU cycles compared to GroupByKey, which forces a full, expensive shuffle and per-key merge of every element. For the Google Professional Data Engineer exam, this scenario tests your understanding of Dataflow’s optimization strategies for high-volume streaming pipelines, where a common trap is assuming GroupByKey is always necessary for aggregation. Instead, Combine.globally with a fanout distributes the merging load across workers, maintaining data freshness with a 10-minute processing time trigger while preventing worker backpressure. Memory tip: think “combine before shuffle” — partial aggregation reduces the data pile each worker must handle, keeping your CPU cool and your pipeline on time.
PDE Designing data processing systems Practice Question
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 Dataflow streaming job is processing high-volume sensor data from thousands of IoT devices. The job uses global windows with a 10-minute processing time trigger. Recently, the job's CPU utilization is nearly 100% and it is falling behind. Which action is most likely to reduce CPU load while maintaining data freshness?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
Replace GroupByKey with Combine.globally and use a fanout.
Option C is correct because using `Combine.globally` with a fanout reduces the amount of data shuffled and merged in a single worker, lowering CPU load. In Dataflow, `GroupByKey` triggers a full shuffle and per-key aggregation, which is expensive for high-volume sensor data; `Combine.globally` with a fanout performs partial aggregation on each worker before a final merge, reducing network I/O and CPU cycles. This maintains data freshness because the 10-minute processing time trigger still fires on time, but with less per-element overhead.
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.
- ✗
Increase the number of workers to distribute the load.
Why it's wrong here
Scaling out may alleviate CPU per worker but doesn't reduce total CPU; also, autoscaling should handle this. The issue is likely inefficient processing.
- ✗
Change the trigger to event time with a 10-minute allowed lateness.
Why it's wrong here
Event time triggers don't reduce computational load; they change when results are emitted.
- ✓
Replace GroupByKey with Combine.globally and use a fanout.
Why this is correct
Combine.globally with fanout reduces the number of unique keys tracked per worker, lowering CPU usage from grouping large numbers of keys.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use side inputs to broadcast a static lookup table to all workers.
Why it's wrong here
Side inputs add memory overhead and are not a generic solution for CPU-bound transformations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling out workers (Option A) is the universal fix for performance issues, but the trap here is that the real bottleneck is the shuffle-heavy `GroupByKey` operation, not worker count.
Detailed technical explanation
How to think about this question
Under the hood, `GroupByKey` in Dataflow requires a full shuffle where all values for a key must be sent to a single worker, causing high CPU for serialization, network I/O, and state management. `Combine.globally` with a fanout uses a combiner that performs partial aggregation on each worker (e.g., summing counts locally), then sends only the partial result to a final global combine, drastically reducing data volume. In real-world IoT pipelines with millions of events per second, this can cut CPU usage by 50-70% while keeping latency under 10 minutes.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
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: Replace GroupByKey with Combine.globally and use a fanout. — Option C is correct because using `Combine.globally` with a fanout reduces the amount of data shuffled and merged in a single worker, lowering CPU load. In Dataflow, `GroupByKey` triggers a full shuffle and per-key aggregation, which is expensive for high-volume sensor data; `Combine.globally` with a fanout performs partial aggregation on each worker before a final merge, reducing network I/O and CPU cycles. This maintains data freshness because the 10-minute processing time trigger still fires on time, but with less per-element overhead.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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