Quick Answer
The correct answer is to increase the batch size in the training script. This action directly addresses low GPU utilization in SageMaker training because a larger batch size feeds more data samples to the GPU per iteration, keeping its compute cores saturated and reducing idle time. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how hardware utilization impacts training performance; a common trap is assuming that adding more instances (horizontal scaling) will fix the issue, but if each GPU is underutilized, you are simply paying for more idle resources. The key insight is that batch size is a lever for parallelism—too small a batch starves the GPU, while too large a batch may cause memory errors, so you must find the sweet spot. Memory tip: think of the GPU as a hungry beast—a small batch is a snack, a large batch is a feast that keeps it busy.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is using Amazon SageMaker to train a model. The training job is taking too long. The data scientist notices that the GPU utilization is low. Which action should be taken to improve training performance?
Increase the number of training instances.
Why wrong: More instances may not improve per-instance utilization.
Use spot instances to reduce cost.
Why wrong: Does not affect utilization.
Decrease the batch size to reduce memory usage.
Why wrong: Smaller batch size reduces utilization.
Increase the batch size in the training script.
Larger batch size keeps GPU busy.
Answer choices
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 in the training script.
Option C is correct because increasing the batch size can improve GPU utilization by keeping the GPU busy. Option A is wrong because increasing the number of instances may not help if each instance is underutilized. Option B is wrong because using spot instances can reduce cost but does not improve utilization. Option D is wrong because decreasing batch size would reduce utilization further.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the number of training instances.
Why it's wrong here
More instances may not improve per-instance utilization.
Use spot instances to reduce cost.
Why it's wrong here
Does not affect utilization.
Decrease the batch size to reduce memory usage.
Why it's wrong here
Smaller batch size reduces utilization.
Increase the batch size in the training script.
Why this is correct
Larger batch size keeps GPU busy.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Identify which MLS-C01 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
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the batch size in the training script. — Option C is correct because increasing the batch size can improve GPU utilization by keeping the GPU busy. Option A is wrong because increasing the number of instances may not help if each instance is underutilized. Option B is wrong because using spot instances can reduce cost but does not improve utilization. Option D is wrong because decreasing batch size would reduce utilization further.
Identify which MLS-C01 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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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