- A
Increase the number of training instances.
Why wrong: More instances may not improve per-instance utilization.
- B
Use spot instances to reduce cost.
Why wrong: Does not affect utilization.
- C
Decrease the batch size to reduce memory usage.
Why wrong: Smaller batch size reduces utilization.
- D
Increase the batch size in the training script.
Larger batch size keeps GPU busy.
Question 373 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 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?
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 in the training script.
Low GPU utilization during training typically indicates that the GPU is waiting for data, often due to a small batch size that underutilizes the GPU's parallel processing capacity. Increasing the batch size allows the GPU to process more samples per step, improving computational efficiency and throughput, which directly addresses the low utilization issue.
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 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
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that low GPU utilization is caused by insufficient compute resources, leading candidates to choose increasing instances (Option A) instead of recognizing it as a data pipeline or batch size issue.
Detailed technical explanation
How to think about this question
GPU utilization is maximized when the batch size is large enough to saturate the GPU's compute units and memory bandwidth. In practice, the optimal batch size depends on the model architecture and GPU memory; for example, on a V100 GPU, batch sizes of 64 or 128 are common for image classification tasks. However, excessively large batch sizes can lead to convergence issues or out-of-memory errors, so tuning should be done incrementally while monitoring GPU memory.
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 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — 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 in the training script. — Low GPU utilization during training typically indicates that the GPU is waiting for data, often due to a small batch size that underutilizes the GPU's parallel processing capacity. Increasing the batch size allows the GPU to process more samples per step, improving computational efficiency and throughput, which directly addresses the low utilization issue.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 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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