- A
The batch size per GPU is too large.
Why wrong: Would likely cause OOM, not low utilization.
- B
The MirroredStrategy is not properly configured.
Why wrong: Usually works out-of-the-box; low utilization more likely I/O.
- C
The data loading from Cloud Storage is a bottleneck.
I/O bottleneck starves GPUs, causing low utilization.
- D
The model is too small for distributed training.
Why wrong: Small models may still show speedup if pipeline is efficient.
Question 649 of 1,000
mediumMultiple ChoiceObjective-mapped
How to Fix Low GPU Utilization in Distributed Training Due to Data Pipeline Bottleneck
This PMLE practice question tests your understanding of pmle 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 team has a prototype image classification model trained on a small dataset using TensorFlow Keras on a single GPU. They need to train on a larger dataset (1 million images) using a distributed strategy on Vertex AI with 8 GPUs. They implement a MirroredStrategy for data parallelism. During the first few epochs, the training speed does not improve significantly compared to a single GPU, and GPU utilization is low. The data is stored as JPEG files in Cloud Storage, and the input pipeline uses tf.data with map to decode images. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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.
Quick Answer
The answer is that the data loading from Cloud Storage is the bottleneck causing low GPU utilization in distributed training. This occurs because the tf.data pipeline, which decodes JPEG images from Cloud Storage, becomes I/O-bound when scaled across 8 GPUs; the single-GPU pipeline cannot feed data fast enough to keep all GPUs busy, leading to idle compute resources. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of distributed training bottlenecks—specifically that data parallelism amplifies pre-existing I/O constraints, not model size or strategy misconfiguration. A common trap is assuming more GPUs automatically improve throughput, but the real limiter is often the input pipeline’s ability to prefetch, cache, or parallelize reads. Memory tip: think “GPUs starve without data speed”—prioritize pipeline optimization over hardware scaling.
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
✓
The data loading from Cloud Storage is a bottleneck.
Option C is correct because reading and decoding JPEG images from Cloud Storage can be I/O-bound, causing low GPU utilization. Even though MirroredStrategy is used, if the input pipeline cannot supply data fast enough, GPUs will spend time waiting for data. Option A is incorrect because a large batch size per GPU typically increases memory usage and computational load, not decreases utilization; it might even improve utilization if the model fits. Option B is incorrect because MirroredStrategy is a straightforward configuration for synchronous data parallelism and does not require complex tuning; it is likely configured correctly. Option D is incorrect because the model size does not directly cause low GPU utilization; even a small model can saturate GPUs if the data pipeline is efficient. The primary bottleneck here is the data loading from Cloud Storage.
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.
- ✗
The batch size per GPU is too large.
Why it's wrong here
Would likely cause OOM, not low utilization.
- ✗
The MirroredStrategy is not properly configured.
Why it's wrong here
Usually works out-of-the-box; low utilization more likely I/O.
- ✓
The data loading from Cloud Storage is a bottleneck.
Why this is correct
I/O bottleneck starves GPUs, causing low utilization.
Clue confirmation
The clue words "first", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model is too small for distributed training.
Why it's wrong here
Small models may still show speedup if pipeline is efficient.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Small models may still show speedup if pipeline is efficient.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The data loading from Cloud Storage is a bottleneck. — Option C is correct because reading and decoding JPEG images from Cloud Storage can be I/O-bound, causing low GPU utilization. Even though MirroredStrategy is used, if the input pipeline cannot supply data fast enough, GPUs will spend time waiting for data. Option A is incorrect because a large batch size per GPU typically increases memory usage and computational load, not decreases utilization; it might even improve utilization if the model fits. Option B is incorrect because MirroredStrategy is a straightforward configuration for synchronous data parallelism and does not require complex tuning; it is likely configured correctly. Option D is incorrect because the model size does not directly cause low GPU utilization; even a small model can saturate GPUs if the data pipeline is efficient. The primary bottleneck here is the data loading from Cloud Storage.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "first", "most likely". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 24, 2026
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