Quick Answer
The answer is to decrease the batch size. This resolves the out-of-memory error during training because reducing the batch size lowers the amount of data loaded into GPU or CPU memory per training step, directly cutting peak memory usage. In SageMaker, the instance’s memory must hold model parameters, activations, and the batch itself; a smaller batch frees up enough headroom for the training job to complete without crashing. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of memory bottlenecks in distributed or single-instance training—a common trap is to suggest upgrading the instance type first, but decreasing batch size is often the fastest, most cost-effective fix. Remember the memory tip: “Smaller batches, bigger memory headroom.”
This MLS-C01 practice question tests your understanding of modeling. 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 is using SageMaker to train a model, but the training job fails with an out-of-memory error. Which action should the data scientist take to resolve this issue?
Use a larger instance type for training
Why wrong: Larger instance may help but is not a direct fix.
Decrease the batch size
Smaller batches use less memory.
Increase the learning rate
Why wrong: Learning rate does not affect memory.
Increase the number of layers
Why wrong: More layers increase 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
Decrease the batch size
Decreasing the batch size reduces the memory footprint per training step, directly addressing the out-of-memory (OOM) error. In SageMaker, the training instance's GPU or CPU memory is shared between model parameters, activations, and the batch data; a smaller batch size lowers the peak memory usage, allowing the training job to complete without exceeding the instance's memory limit.
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.
Use a larger instance type for training
Why it's wrong here
Larger instance may help but is not a direct fix.
Decrease the batch size
Why this is correct
Smaller batches use less memory.
Related concept
Read the scenario before looking for a memorised answer.
Increase the learning rate
Why it's wrong here
Learning rate does not affect memory.
Increase the number of layers
Why it's wrong here
More layers increase memory.
Common exam traps
The trap here is that candidates often default to scaling up infrastructure (larger instance) instead of optimizing hyperparameters like batch size, which is a more immediate and cost-effective fix for OOM errors in SageMaker.
Detailed technical explanation
Under the hood, the memory bottleneck often arises from storing intermediate activations for backpropagation; reducing batch size directly cuts the number of activation tensors held in memory. In SageMaker, the training job's memory limit is defined by the instance type (e.g., ml.p3.2xlarge has 16 GB GPU memory), and batch size is a hyperparameter that can be tuned without changing the instance. A real-world scenario is training large transformer models where even a single batch can exceed memory; practitioners use gradient accumulation to simulate larger batches without increasing memory usage.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Decrease the batch size — Decreasing the batch size reduces the memory footprint per training step, directly addressing the out-of-memory (OOM) error. In SageMaker, the training instance's GPU or CPU memory is shared between model parameters, activations, and the batch data; a smaller batch size lowers the peak memory usage, allowing the training job to complete without exceeding the instance's memory limit.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.