- A
Increase the batch size to match GPU memory
Larger batch size increases the amount of computation per step, keeping GPUs more fully utilized.
- B
Reduce the number of data loading workers
Why wrong: Reducing workers can exacerbate data loading bottlenecks, decreasing GPU utilization.
- C
Use mixed precision training
Why wrong: Mixed precision can accelerate computation but does not directly increase GPU utilization if the batch size is small.
- D
Enable SageMaker Managed Warm Pools
Why wrong: Warm Pools are used for inference, not training.
Question 792 of 1,000
hardMultiple ChoiceObjective-mapped
Improving Low GPU Utilization with Larger Batch Sizes
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 machine learning engineer is training a deep learning model using TensorFlow in SageMaker. The training runs on an ml.p3.16xlarge instance (8 GPUs). The engineer notices that GPU utilization is low (~30%) and time per epoch is high. The model uses a custom training loop. Which configuration change is most likely to improve GPU utilization?
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
✓
Increase the batch size to match GPU memory
Low GPU utilization (~30%) with a custom training loop on an 8-GPU instance typically indicates that each GPU is not receiving enough work to keep its compute units busy. Increasing the batch size to match GPU memory allows each GPU to process more data per step, improving arithmetic intensity and reducing the overhead of frequent weight updates, which directly raises GPU utilization.
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 batch size to match GPU memory
Why this is correct
Larger batch size increases the amount of computation per step, keeping GPUs more fully utilized.
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.
- ✗
Reduce the number of data loading workers
Why it's wrong here
Reducing workers can exacerbate data loading bottlenecks, decreasing GPU utilization.
- ✗
Use mixed precision training
Why it's wrong here
Mixed precision can accelerate computation but does not directly increase GPU utilization if the batch size is small.
- ✗
Enable SageMaker Managed Warm Pools
Why it's wrong here
Warm Pools are used for inference, not training.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common misconception in AWS exams is that mixed precision training is the universal fix for low GPU utilization, when in reality it only helps if the model is memory-bound or if FP32 is causing out-of-memory errors, not when the batch size is too small.
Detailed technical explanation
How to think about this question
Under the hood, GPU utilization is driven by the number of concurrent threads and the amount of computation per kernel launch. With a small batch size, each forward/backward pass underutilizes the GPU's streaming multiprocessors, leading to low occupancy. Increasing batch size saturates the GPU memory bandwidth and compute cores, often raising utilization above 80%. In practice, the optimal batch size is limited by GPU memory; tools like `nvidia-smi` or SageMaker's CloudWatch GPU metrics can help identify the memory ceiling.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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 MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Increase the batch size to match GPU memory — Low GPU utilization (~30%) with a custom training loop on an 8-GPU instance typically indicates that each GPU is not receiving enough work to keep its compute units busy. Increasing the batch size to match GPU memory allows each GPU to process more data per step, improving arithmetic intensity and reducing the overhead of frequent weight updates, which directly raises GPU utilization.
What should I do if I get this MLA-C01 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: Jul 4, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.
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