Quick Answer
The correct answer is to reduce the batch size in the training script. This works because the GPU’s memory must hold the entire batch’s activations, gradients, and intermediate tensors during a single forward and backward pass; cutting the batch size directly reduces the memory footprint per iteration, freeing enough space to avoid the OutOfMemoryError. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of GPU memory management during SageMaker training—a common trap is assuming you need a larger instance, but that wastes cost, while distributed training actually increases per-node memory pressure. Remember the memory tip: “Batch size is the gas pedal—too much, and you blow the engine; dial it back to keep the GPU cool.”
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 data scientist is training a neural network on a GPU instance in Amazon SageMaker. The training job fails with an 'OutOfMemoryError'. Which action should the data scientist take to resolve this issue?
Enable automatic hyperparameter tuning.
Why wrong: Hyperparameter tuning does not directly reduce memory usage.
Switch to distributed training across multiple instances.
Why wrong: Distributed training can increase memory usage per node.
Use a smaller instance type with less GPU memory.
Why wrong: Smaller instance has less memory, not more.
Reduce the batch size in the training script.
Smaller batch size reduces memory footprint.
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.
Option C is correct because reducing the batch size reduces memory usage per iteration. Option A is wrong because using a smaller instance type would have less memory. Option B is wrong because hyperparameter tuning does not directly reduce memory. Option D is wrong because distributed training typically increases memory usage per node.
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.
Enable automatic hyperparameter tuning.
Why it's wrong here
Hyperparameter tuning does not directly reduce memory usage.
Switch to distributed training across multiple instances.
Why it's wrong here
Distributed training can increase memory usage per node.
Use a smaller instance type with less GPU memory.
Why it's wrong here
Smaller instance has less memory, not more.
Reduce the batch size in the training script.
Why this is correct
Smaller batch size reduces memory footprint.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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. — Option C is correct because reducing the batch size reduces memory usage per iteration. Option A is wrong because using a smaller instance type would have less memory. Option B is wrong because hyperparameter tuning does not directly reduce memory. Option D is wrong because distributed training typically increases memory usage per node.
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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