Quick Answer
The answer is gradient accumulation, as it directly addresses the need to reduce training time in Amazon SageMaker without significant accuracy loss. By simulating a larger batch size through multiple small-batch forward and backward passes before performing a single optimizer update, gradient accumulation allows the model to converge in fewer total steps while staying within GPU memory limits. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this technique tests your understanding of distributed training optimization and memory management—a common trap is confusing it with early stopping or regularization, which do not accelerate training. Remember that gradient accumulation effectively increases the batch size without requiring more instances, making it a go-to strategy when scaling horizontally has already been exhausted. Memory tip: “Accumulate gradients, not instances.”
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 team is using Amazon SageMaker to train a deep learning model. The training job is taking too long, and they want to reduce training time without significant accuracy loss. They have already tried increasing the number of instances. Which technique should they consider next?
Increase L2 regularization
Why wrong: Regularization does not reduce training time.
Reduce model complexity
Why wrong: Reducing complexity may cause underfitting.
Gradient accumulation
Gradient accumulation simulates larger batch sizes, improving convergence speed.
Early stopping
Why wrong: Early stopping stops training early but does not reduce per-epoch time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Gradient accumulation
Option D is correct because gradient accumulation allows simulating larger batch sizes without increasing memory, often speeding up convergence. Option A is wrong because early stopping may stop too early. Option B is wrong because reducing model complexity may cause underfitting. Option C is wrong because L2 regularization does not reduce training time.
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 L2 regularization
Why it's wrong here
Regularization does not reduce training time.
Reduce model complexity
Why it's wrong here
Reducing complexity may cause underfitting.
Gradient accumulation
Why this is correct
Gradient accumulation simulates larger batch sizes, improving convergence speed.
Related concept
Read the scenario before looking for a memorised answer.
Early stopping
Why it's wrong here
Early stopping stops training early but does not reduce per-epoch time.
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 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 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 — 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: Gradient accumulation — Option D is correct because gradient accumulation allows simulating larger batch sizes without increasing memory, often speeding up convergence. Option A is wrong because early stopping may stop too early. Option B is wrong because reducing model complexity may cause underfitting. Option C is wrong because L2 regularization does not reduce training time.
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.