Quick Answer
The answer is to increase the number of trees and limit the maximum depth of each tree. Increasing the number of trees reduces variance by averaging predictions across a larger ensemble, which smooths out noise and stabilizes the model, while limiting the maximum depth prevents individual trees from memorizing training data, thus reducing overfitting. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of bias-variance tradeoff in ensemble methods; a common trap is confusing tree count with depth—adding more trees rarely overfits, but deeper trees always risk overfitting. To reduce overfitting in a random forest classifier, remember the mnemonic “More Trees, Less Depth”—more trees lower variance, less depth lowers bias from over-specialization.
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 data scientist is training a random forest classifier on Amazon SageMaker and wants to reduce overfitting. Which TWO actions should the scientist take? (Choose TWO.)
Increase the number of features considered per split
Why wrong: More features increase tree correlation and overfitting.
Increase the maximum depth of trees
Why wrong: Deeper trees overfit more.
Decrease the number of trees
Why wrong: Fewer trees increase variance.
Limit the maximum depth of trees
Shallow trees reduce overfitting.
Increase the number of trees
More trees reduce variance.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Limit the maximum depth of trees
Increasing the number of trees reduces variance, and limiting the maximum depth prevents overfitting. Option A is wrong because increasing max depth increases overfitting. Option D is wrong because reducing the number of trees increases variance. Option E is wrong because increasing the number of features increases tree correlation and may increase overfitting.
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 number of features considered per split
Why it's wrong here
More features increase tree correlation and overfitting.
Increase the maximum depth of trees
Why it's wrong here
Deeper trees overfit more.
Decrease the number of trees
Why it's wrong here
Fewer trees increase variance.
Limit the maximum depth of trees
Why this is correct
Shallow trees reduce overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Increase the number of trees
Why this is correct
More trees reduce variance.
Related concept
Read the scenario before looking for a memorised answer.
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Limit the maximum depth of trees — Increasing the number of trees reduces variance, and limiting the maximum depth prevents overfitting. Option A is wrong because increasing max depth increases overfitting. Option D is wrong because reducing the number of trees increases variance. Option E is wrong because increasing the number of features increases tree correlation and may increase overfitting.
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
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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.
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