This MLA-C01 practice question tests your understanding of ml model development. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 machine learning engineer is training a deep learning model using TensorFlow in SageMaker. The training runs on an ml.p3.16xlarge instance (8 GPUs). The engineer notices that GPU utilization is low (~30%) and time per epoch is high. The model uses a custom training loop. Which configuration change is most likely to improve GPU utilization?
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 to match GPU memory
Option C is correct because increasing the batch size increases the computational work per GPU, keeping them busier and improving utilization. Option A (mixed precision) can improve throughput but not necessarily utilization if batch size remains small. Option B (SageMaker Managed Warm Pools) is for inference. Option D (reducing data loading workers) could worsen data starvation, decreasing 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 to match GPU memory
Why this is correct
Larger batch size increases the amount of computation per step, keeping GPUs more fully utilized.
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.
✗
Reduce the number of data loading workers
Why it's wrong here
Reducing workers can exacerbate data loading bottlenecks, decreasing GPU utilization.
✗
Use mixed precision training
Why it's wrong here
Mixed precision can accelerate computation but does not directly increase GPU utilization if the batch size is small.
✗
Enable SageMaker Managed Warm Pools
Why it's wrong here
Warm Pools are used for inference, not training.
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 MLA-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.
ML Model Development — This question tests ML Model Development — 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 to match GPU memory — Option C is correct because increasing the batch size increases the computational work per GPU, keeping them busier and improving utilization. Option A (mixed precision) can improve throughput but not necessarily utilization if batch size remains small. Option B (SageMaker Managed Warm Pools) is for inference. Option D (reducing data loading workers) could worsen data starvation, decreasing utilization.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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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Question Discussion
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