The answer is to reduce the batch size, use gradient accumulation, or switch to a larger instance like ml.p3.16xlarge. These three actions directly address the root cause of an OutOfMemory error in SageMaker deep learning, which occurs when the GPU or CPU memory cannot hold the entire batch of data, model parameters, and intermediate activations during training. Reducing the batch size lowers the memory footprint per step, while gradient accumulation simulates a larger batch without increasing peak memory usage, and a larger instance provides more total memory capacity. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of memory bottlenecks in distributed training and custom containers—a common trap is confusing training duration (epochs) with per-step memory consumption. Remember the mnemonic "BAG": Batch size, Accumulation, and a bigger GPU instance.
MLS-C01 Modeling Practice Question
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 data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this issue? (Choose 3.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use gradient accumulation to simulate larger batch sizes.
OutOfMemory can be resolved by reducing batch size, using gradient accumulation, or using a larger instance. Option A (reducing epochs) does not affect memory usage per batch. Option E (increasing learning rate) might cause instability but not directly memory.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Use gradient accumulation to simulate larger batch sizes.
Why this is correct
Gradient accumulation allows training with effectively larger batch without memory increase.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the number of training epochs.
Why it's wrong here
Epochs do not affect per-step memory usage.
✓
Reduce the batch size.
Why this is correct
Smaller batch size reduces memory consumption.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use an instance type with more memory, such as ml.p3.16xlarge.
Why this is correct
Larger instance provides more GPU 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 usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use gradient accumulation to simulate larger batch sizes. — OutOfMemory can be resolved by reducing batch size, using gradient accumulation, or using a larger instance. Option A (reducing epochs) does not affect memory usage per batch. Option E (increasing learning rate) might cause instability but not directly memory.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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
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