The answer is to use gradient accumulation, which resolves insufficient GPU memory in SageMaker without altering model architecture. This technique works by splitting a large batch into smaller micro-batches, computing gradients on each, and accumulating them before updating model weights—this drastically reduces per-step GPU memory consumption while preserving the effective batch size for training stability. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of memory optimization strategies under resource constraints; a common trap is assuming that adding more GPUs or reducing the learning rate will fix memory errors, but neither reduces per-GPU memory usage. Remember the key distinction: gradient accumulation decouples batch size from memory footprint, allowing you to train large models on limited hardware. A helpful memory tip is “accumulate gradients, not memory.”
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 machine learning team is using SageMaker to train a deep learning model. The training job is failing due to insufficient GPU memory. Which approach should the team take to resolve this issue without changing the model architecture?
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 reduce the effective batch size per step.
Option B is correct because gradient accumulation simulates larger batch sizes without increasing memory per step. Option A is wrong because increasing batch size increases memory usage. Option C is wrong because reducing learning rate does not affect memory. Option D is wrong because adding more GPUs to a single instance may not help if memory is already exhausted on each GPU; but the key is to reduce per-GPU memory, which gradient accumulation achieves by using a smaller effective batch size per step.
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.
✗
Increase the batch size.
Why it's wrong here
Larger batch size increases memory consumption.
✓
Use gradient accumulation to reduce the effective batch size per step.
Why this is correct
Gradient accumulation allows training with larger effective batches while keeping per-step memory low.
Related concept
Read the scenario before looking for a memorised answer.
✗
Add more GPUs to the training instance.
Why it's wrong here
Adding GPUs does not reduce per-GPU memory; the model still needs to fit on each GPU.
✗
Decrease 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 reduce the effective batch size per step. — Option B is correct because gradient accumulation simulates larger batch sizes without increasing memory per step. Option A is wrong because increasing batch size increases memory usage. Option C is wrong because reducing learning rate does not affect memory. Option D is wrong because adding more GPUs to a single instance may not help if memory is already exhausted on each GPU; but the key is to reduce per-GPU memory, which gradient accumulation achieves by using a smaller effective batch size per step.
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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