The answer is to increase the batch size in the training script. Low GPU utilization, often below 10%, typically signals a data loading bottleneck where the GPU spends most of its time idle waiting for the next batch of data. By increasing the batch size, you feed more data to the GPU per step, keeping it busier with computation and reducing the relative overhead of data transfer. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of common SageMaker performance pitfalls, where the trap is to immediately blame the instance type rather than the training configuration. A key memory tip is to think of the GPU as a factory: if the conveyor belt (data pipeline) is too slow, you don’t replace the factory—you load more boxes per trip (increase batch size) to keep the machines running.
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 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 often indicates a data loading bottleneck. Increasing the batch size can improve GPU utilization by feeding more data at once, but it may also cause memory issues. Using a larger instance type with more GPU memory could help if the model is large. However, the most common fix is to use SageMaker Pipe Mode or Fast File Mode to stream data efficiently, reducing I/O wait. Among the options, increasing batch size is a direct way to increase 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 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
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
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 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 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.
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 often indicates a data loading bottleneck. Increasing the batch size can improve GPU utilization by feeding more data at once, but it may also cause memory issues. Using a larger instance type with more GPU memory could help if the model is large. However, the most common fix is to use SageMaker Pipe Mode or Fast File Mode to stream data efficiently, reducing I/O wait. Among the options, increasing batch size is a direct way to increase GPU utilization.
What should I do if I get this MLS-C01 question wrong?
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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.