Quick Answer
The answer is to reduce the batch size. This is the correct first step because GPU memory consumption during training is directly proportional to the batch size; each batch holds a fixed number of samples, their labels, and the intermediate activations needed for backpropagation, so halving the batch size roughly halves the memory footprint per training step. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical debugging for SageMaker training jobs, where the common trap is to confuse memory errors with convergence issues—options like reducing the learning rate or adding regularization target overfitting or gradient problems, not memory. A key memory tip: think of the batch size as the “bucket” that fills GPU VRAM; when you get an out-of-memory error, always shrink the bucket first before tweaking the model architecture or data type.
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.
During training of a deep learning model on a GPU instance in SageMaker, the training job fails with an insufficient memory error. Which step should be taken first to resolve this issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Add dropout layers
Why wrong: Reduces overfitting, not memory consumption.
Use a smaller learning rate
Why wrong: Affects convergence, not memory usage.
Use gradient clipping
Why wrong: Prevents gradient explosion, does not reduce memory.
Reduce the batch size
Smaller batch size reduces GPU 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
Option B is correct because reducing the batch size directly decreases GPU memory usage. Option A is wrong because it reduces training time but not memory per step. Option C is wrong because it addresses vanishing gradients, not memory. Option D is wrong because it reduces overfitting, not 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.
Add dropout layers
Why it's wrong here
Reduces overfitting, not memory consumption.
Use a smaller learning rate
Why it's wrong here
Affects convergence, not memory usage.
Use gradient clipping
Why it's wrong here
Prevents gradient explosion, does not reduce memory.
Reduce the batch size
Why this is correct
Smaller batch size reduces GPU memory footprint.
Clue confirmation
The clue word "first" in the question point toward this answer.
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 — Option B is correct because reducing the batch size directly decreases GPU memory usage. Option A is wrong because it reduces training time but not memory per step. Option C is wrong because it addresses vanishing gradients, not memory. Option D is wrong because it reduces overfitting, not 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.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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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