Quick Answer
The correct answer is to increase the instance memory by selecting a larger instance type. This directly resolves the "Resource exhausted: Out of memory" error because SageMaker training jobs allocate model parameters, gradients, and intermediate tensors within the instance’s RAM, and when the dataset or model size exceeds available memory, the kernel terminates. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker instance sizing versus data loading optimizations—a common trap is confusing memory issues with I/O bottlenecks. While Pipe mode or reducing batch size can help with data streaming or gradient accumulation, they do not fix fundamental memory exhaustion from model footprint or large tensors. Remember the mnemonic: "Memory means instance type, not storage or batch size"—when you see "out of memory," always scale up the instance family (e.g., from ml.m5.xlarge to ml.m5.2xlarge) before tweaking hyperparameters.
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 model using Amazon SageMaker with a custom Docker container. The training job fails with an error: 'Resource exhausted: Out of memory'. The training data is stored in S3. What should the data scientist do to resolve this issue?
Increase the instance memory by selecting a larger instance type.
Larger instance provides more memory.
Increase the EBS volume size attached to the training instance.
Why wrong: EBS size does not affect memory.
Use Pipe mode for data loading instead of File mode.
Why wrong: Pipe mode reduces local storage, not memory.
Reduce the batch size in the training script.
Why wrong: Reducing batch size may help but instance memory is the root cause.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the instance memory by selecting a larger instance type.
Option B is correct because 'Out of memory' indicates the instance does not have enough memory. Increasing the instance memory resolves the issue. Option A is wrong because using Pipe mode streams data directly from S3 and can reduce memory usage, but the error is about memory exhaustion, not data loading. Option C is wrong because EBS volume size does not affect memory. Option D is wrong because reducing batch size might help but is not the primary fix; increasing instance memory directly addresses the issue.
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.
Increase the instance memory by selecting a larger instance type.
Why this is correct
Larger instance provides more memory.
Related concept
Read the scenario before looking for a memorised answer.
Increase the EBS volume size attached to the training instance.
Why it's wrong here
EBS size does not affect memory.
Use Pipe mode for data loading instead of File mode.
Why it's wrong here
Pipe mode reduces local storage, not memory.
Reduce the batch size in the training script.
Why it's wrong here
Reducing batch size may help but instance memory is the root cause.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Machine Learning Implementation and Operations — study guide chapter
Learn the concepts, then practise the questions
Machine Learning Implementation and Operations 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
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: Increase the instance memory by selecting a larger instance type. — Option B is correct because 'Out of memory' indicates the instance does not have enough memory. Increasing the instance memory resolves the issue. Option A is wrong because using Pipe mode streams data directly from S3 and can reduce memory usage, but the error is about memory exhaustion, not data loading. Option C is wrong because EBS volume size does not affect memory. Option D is wrong because reducing batch size might help but is not the primary fix; increasing instance memory directly addresses the issue.
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.
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 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.
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.