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 data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist notices that GPU utilization is low (around 30%). Which action 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
Low GPU utilization (around 30%) indicates that the GPU is spending too much time idle while waiting for data batches to be processed. Increasing the batch size allows each training step to process more samples per forward/backward pass, which increases computational load on the GPU and improves hardware utilization. This directly reduces the number of steps needed per epoch, thereby decreasing overall 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
Why this is correct
Larger batch size keeps GPU busy, improving utilization and reducing total training time if the data pipeline can keep up.
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 smaller instance type
Why it's wrong here
Smaller instance has less GPU memory and compute, potentially worsening the problem.
✗
Increase the learning rate
Why it's wrong here
Learning rate affects convergence speed, not GPU utilization directly.
✗
Reduce the batch size
Why it's wrong here
Smaller batch size means less work per GPU step, likely decreasing utilization further.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse low GPU utilization with a need to reduce batch size (thinking smaller batches speed up training), when in fact increasing batch size is the standard remedy to saturate GPU compute and reduce wall-clock time.
Detailed technical explanation
How to think about this question
Under the hood, GPU utilization is often limited by the data loading pipeline (I/O bottleneck) or by small batch sizes that fail to saturate the GPU's parallel compute units. Increasing batch size also improves the accuracy of gradient estimates (reducing variance) and can enable larger effective learning rates, but must be balanced against memory constraints. In SageMaker, the batch size is set in the training script (e.g., via TensorFlow's `tf.data.Dataset.batch()` or PyTorch's `DataLoader`), and the instance type's GPU memory (e.g., 16 GB on a p3.2xlarge) dictates the maximum feasible batch size.
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.
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 batch size — Low GPU utilization (around 30%) indicates that the GPU is spending too much time idle while waiting for data batches to be processed. Increasing the batch size allows each training step to process more samples per forward/backward pass, which increases computational load on the GPU and improves hardware utilization. This directly reduces the number of steps needed per epoch, thereby decreasing overall 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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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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