Quick Answer
The answer is to use a larger instance type, such as ml.m5.2xlarge, because the OutOfMemory error in SageMaker training directly indicates that the chosen instance lacks sufficient RAM to hold the model parameters, gradients, and data batches during computation. Scaling up to a larger instance type, like moving from ml.m5.xlarge to ml.m5.2xlarge, doubles the available memory, providing the most straightforward and efficient fix without altering your code or data pipeline. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s resource allocation and the distinction between horizontal scaling (increasing instance count) and vertical scaling (increasing instance size). A common trap is confusing instance count with memory per instance—adding more instances does not increase the memory available to a single training job, whereas a larger instance directly resolves the memory bottleneck. Remember the mnemonic: "Out of memory? Go up a size, not out to more."
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 company uses Amazon SageMaker to train a model. The training job fails with an 'OutOfMemory' error. The training data is stored in S3 and the instance type is ml.m5.xlarge. What is the most efficient way to resolve this issue?
Enable managed spot training
Why wrong: Spot instances do not affect memory.
Reduce the batch size in the training script
Why wrong: This may help but is not the most efficient solution.
Increase the number of instances using distributed training
Why wrong: Distributed training does not increase memory per instance.
Use a larger instance type, such as ml.m5.2xlarge
Larger instance provides more memory.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a larger instance type, such as ml.m5.2xlarge
Option C is correct: Increasing the instance type provides more memory. Option A (increasing instance count) does not increase memory per instance. Option B (reducing batch size) may help but is less efficient. Option D (enabling spot instances) does not address 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.
Enable managed spot training
Why it's wrong here
Spot instances do not affect memory.
Reduce the batch size in the training script
Why it's wrong here
This may help but is not the most efficient solution.
Increase the number of instances using distributed training
Why it's wrong here
Distributed training does not increase memory per instance.
Use a larger instance type, such as ml.m5.2xlarge
Why this is correct
Larger instance provides more memory.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Use a larger instance type, such as ml.m5.2xlarge — Option C is correct: Increasing the instance type provides more memory. Option A (increasing instance count) does not increase memory per instance. Option B (reducing batch size) may help but is less efficient. Option D (enabling spot instances) does not address 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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