The correct answer is to increase the number of data loading workers. This directly addresses low GPU utilization during SageMaker training by improving data throughput, as the GPU is idling because the data pipeline cannot feed it fast enough—a classic I/O bottleneck. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to diagnose performance bottlenecks in distributed training, where high CPU utilization combined with low GPU usage signals that preprocessing or data loading is the constraint. A common trap is to assume larger batch sizes will help, but that can increase memory pressure without fixing the pipeline speed. Remember the memory tip: “If the GPU is waiting, the data is hesitating”—so unblock the pipeline by adding more workers to keep the GPU fed.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 uses Amazon SageMaker to train a deep learning model for image classification. The training job is taking longer than expected. The data scientist observes that GPU utilization is low (around 30%) and CPU utilization is high. Which action is most likely to 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 number of data loading workers
Option C is correct because low GPU utilization indicates that the data pipeline is not feeding data fast enough, causing the GPU to idle. Increasing the number of data loading workers can improve data throughput. Option A is wrong because larger batch sizes may increase memory usage and not directly address the bottleneck. Option B is wrong because reducing batch size may further underutilize GPU. Option D is wrong because increasing learning rate does not address data loading bottleneck.
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.
✗
Reduce the batch size
Why it's wrong here
Smaller batch sizes may lead to more frequent weight updates but can further underutilize GPU.
✗
Increase the batch size
Why it's wrong here
Larger batch sizes may increase memory usage and not improve data loading speed.
✗
Increase the learning rate
Why it's wrong here
Learning rate affects convergence speed, not data loading or GPU utilization.
✓
Increase the number of data loading workers
Why this is correct
More data loading workers can parallelize data preprocessing and reduce I/O bottleneck, improving GPU utilization.
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.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of data loading workers — Option C is correct because low GPU utilization indicates that the data pipeline is not feeding data fast enough, causing the GPU to idle. Increasing the number of data loading workers can improve data throughput. Option A is wrong because larger batch sizes may increase memory usage and not directly address the bottleneck. Option B is wrong because reducing batch size may further underutilize GPU. Option D is wrong because increasing learning rate does not address data loading bottleneck.
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.
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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
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