Quick Answer
The answer is to reduce the batch size in the training script. This is the correct action because GPU memory optimization for SageMaker training directly hinges on the batch size: each training iteration processes a batch of samples, and the memory footprint scales linearly with the number of samples held in the GPU’s VRAM. By halving the batch size, you roughly halve the memory consumed by activations and gradients, freeing enough space to avoid the OutOfMemory error without altering the model architecture. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of practical debugging for resource-constrained training jobs—a common trap is to mistakenly increase the instance type or change the loss function, but those either add cost or shift the problem domain. Remember the memory tip: “Batch size down, memory freed; architecture stays, problem recedes.”
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 team is training a large NLP model using SageMaker. The training job fails with an OutOfMemory error. The instance type is ml.p3.2xlarge with 61 GB GPU memory. Which action should the team take to resolve the issue without changing the model architecture?
Switch to a regression model
Why wrong: Changing the model type does not directly address memory; it changes the task.
Increase the number of epochs
Why wrong: More epochs increase training time, not memory per step.
Enable SageMaker Managed Warm Pools
Why wrong: Warm pools reduce startup time, not memory usage.
Reduce the batch size in the training script
Smaller batch size reduces GPU memory consumption per step.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Reduce the batch size in the training script
Reducing the batch size decreases GPU memory usage per iteration. Option A is correct. Option B changes the problem to regression. Option C increases memory usage. Option D is unrelated to GPU memory.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Switch to a regression model
Why it's wrong here
Changing the model type does not directly address memory; it changes the task.
Increase the number of epochs
Why it's wrong here
More epochs increase training time, not memory per step.
Enable SageMaker Managed Warm Pools
Why it's wrong here
Warm pools reduce startup time, not memory usage.
Reduce the batch size in the training script
Why this is correct
Smaller batch size reduces GPU memory consumption per step.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the batch size in the training script — Reducing the batch size decreases GPU memory usage per iteration. Option A is correct. Option B changes the problem to regression. Option C increases memory usage. Option D is unrelated to GPU memory.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 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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