- A
Increase the batch size in the training script.
Increasing batch size can improve GPU utilization by processing more data per step.
- B
Use a different optimizer that requires less computation.
Why wrong: Optimizer choice has minimal impact on GPU utilization compared to data throughput.
- C
Switch to a smaller instance type to reduce data transfer overhead.
Why wrong: A smaller instance may have less GPU memory and could worsen the bottleneck.
- D
Reduce the size of the training dataset.
Why wrong: Reducing dataset size is not a practical solution and does not address the utilization issue.
Question 438 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. 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 data scientist is training a deep learning model on a large dataset using SageMaker. The training job is taking too long. Upon reviewing the CloudWatch logs, the scientist notices that the GPU utilization is below 10% most of the time. Which change is MOST likely to improve GPU utilization and reduce training time?
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 in the training script.
Low GPU utilization (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total training time.
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 in the training script.
Why this is correct
Increasing batch size can improve GPU utilization by processing more data per step.
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.
- ✗
Use a different optimizer that requires less computation.
Why it's wrong here
Optimizer choice has minimal impact on GPU utilization compared to data throughput.
- ✗
Switch to a smaller instance type to reduce data transfer overhead.
Why it's wrong here
A smaller instance may have less GPU memory and could worsen the bottleneck.
- ✗
Reduce the size of the training dataset.
Why it's wrong here
Reducing dataset size is not a practical solution and does not address the utilization issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates mistakenly think reducing instance size or dataset size will speed up training, when in fact the core issue is underutilization of the existing GPU due to insufficient work per step.
Detailed technical explanation
How to think about this question
GPU utilization is often bottlenecked by data loading and preprocessing (I/O bound) rather than compute. Increasing batch size increases the arithmetic intensity (ratio of compute to memory access), allowing the GPU to perform more floating-point operations per second (FLOPS) and better exploit tensor core parallelism. However, very large batch sizes may require gradient accumulation or learning rate tuning to maintain model accuracy, as they can lead to sharp minima and poor generalization.
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 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 (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total training time.
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.
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 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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