- A
Reduce the window size from 10 minutes to 1 minute to decrease the amount of data per window.
Why wrong: Smaller windows increase the number of open windows, which can worsen the high uncommitted bytes error.
- B
Increase the number of worker machines to handle higher throughput.
Why wrong: Increasing workers can improve throughput but does not directly address the uncommitted bytes issue caused by too many open windows.
- C
Use a global window with a trigger that fires early based on element count to reduce the number of open windows.
A global window with early triggers can reduce the number of panes and mitigate the high uncommitted bytes problem.
- D
Set a maximum number of workers and use a Pub/Sub flow control setting to limit incoming messages.
Why wrong: Flow control limits the rate of input but does not address the windowing issue; the pipeline may still experience backpressure.
Quick Answer
The correct answer is to use a global window with a trigger that fires early based on element count to reduce the number of open windows. This resolves the Dataflow high uncommitted bytes error because the root cause is window fan-out: during high traffic, a 10-minute fixed window creates too many simultaneous open windows, each holding partial data in memory until the window closes, which exceeds the default 200 MB uncommitted bytes limit. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataflow’s streaming engine memory management and the trade-offs between windowing strategies and triggers. A common trap is to assume scaling workers or reducing the window duration will fix the issue, but the real problem is the number of concurrent open windows, not throughput or parallelism. Memory tip: think “fan-out, not scale-out”—when you see high uncommitted bytes, look to collapse windows or add early triggers, not add more workers.
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 company runs a Dataflow pipeline that reads from Pub/Sub, aggregates events in a 10-minute fixed window, and writes to BigQuery. Recently, the pipeline has been failing with 'high uncommitted bytes' errors during periods of high traffic. What is the most likely cause and recommended action?
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
Use a global window with a trigger that fires early based on element count to reduce the number of open windows.
The 'high uncommitted bytes' error in Dataflow occurs when the system holds too much data in memory across many open windows, exceeding the default 200 MB limit. Using a global window with an early trigger based on element count reduces the number of simultaneous open windows and allows data to be committed more frequently, preventing memory pressure. This approach is recommended over reducing window size or scaling workers because the root cause is window fan-out, not throughput or parallelism.
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.
- ✗
Reduce the window size from 10 minutes to 1 minute to decrease the amount of data per window.
Why it's wrong here
Smaller windows increase the number of open windows, which can worsen the high uncommitted bytes error.
- ✗
Increase the number of worker machines to handle higher throughput.
Why it's wrong here
Increasing workers can improve throughput but does not directly address the uncommitted bytes issue caused by too many open windows.
- ✓
Use a global window with a trigger that fires early based on element count to reduce the number of open windows.
Why this is correct
A global window with early triggers can reduce the number of panes and mitigate the high uncommitted bytes problem.
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.
- ✗
Set a maximum number of workers and use a Pub/Sub flow control setting to limit incoming messages.
Why it's wrong here
Flow control limits the rate of input but does not address the windowing issue; the pipeline may still experience backpressure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling workers or reducing window size solves memory pressure, when the real issue is the number of open windows in a stateful pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Dataflow's streaming engine uses a 'commit window' mechanism where each open window accumulates state until it is committed; with fixed windows, every distinct key creates a separate window, leading to O(keys * windows) state objects. A global window with an early trigger (e.g., after 1000 elements) allows the pipeline to flush data to BigQuery incrementally, keeping only a single window per key and drastically reducing the number of uncommitted bundles. In real-world scenarios, this pattern is critical when processing high-cardinality keys (e.g., user IDs) with long windows, where the number of open windows can exceed memory limits even with autoscaling.
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.
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?
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: Use a global window with a trigger that fires early based on element count to reduce the number of open windows. — The 'high uncommitted bytes' error in Dataflow occurs when the system holds too much data in memory across many open windows, exceeding the default 200 MB limit. Using a global window with an early trigger based on element count reduces the number of simultaneous open windows and allows data to be committed more frequently, preventing memory pressure. This approach is recommended over reducing window size or scaling workers because the root cause is window fan-out, not throughput or parallelism.
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.
About these practice questions
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Last reviewed: Jun 30, 2026
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.
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