Quick Answer
The answer is to decrease the maximum depth of each tree. This parameter directly combats overfitting by limiting how many splits each decision tree can make, preventing individual trees from memorizing noise and outliers in the training data. In a random forest, while the ensemble averages out variance, overly deep trees still capture spurious patterns, so capping depth acts as a critical regularization technique. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of the bias-variance tradeoff within ensemble methods—a common trap is assuming that more trees always reduce overfitting, when in fact depth control is the primary lever. Remember the memory tip: “Shallow trees, strong forest”—shallower trees force simpler decision boundaries, ensuring the forest generalizes rather than memorizes.
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 model. During hyperparameter tuning, which parameter is MOST effective at reducing overfitting?
Increase the number of trees
Why wrong: More trees can reduce overfitting but not as directly as depth.
Increase the number of features considered per split
Why wrong: More features may lead to overfitting.
Decrease the maximum depth of each tree
Shallow trees generalize better.
Increase the maximum depth of each tree
Why wrong: Deeper trees increase overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Decrease the maximum depth of each tree
Decreasing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and outliers in the training data. This directly reduces overfitting by enforcing simpler decision boundaries, which is a core regularization technique for ensemble methods like Random Forest.
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 trees
Why it's wrong here
More trees can reduce overfitting but not as directly as depth.
Increase the number of features considered per split
Why it's wrong here
More features may lead to overfitting.
Decrease the maximum depth of each tree
Why this is correct
Shallow trees generalize better.
Related concept
Read the scenario before looking for a memorised answer.
Increase the maximum depth of each tree
Why it's wrong here
Deeper trees increase overfitting.
Common exam traps
AWS often tests the misconception that adding more trees always reduces overfitting, but the trap is that while more trees stabilize predictions, they do not address the root cause of overfitting from overly complex individual trees.
Detailed technical explanation
Random Forest reduces overfitting by averaging many decorrelated trees; however, individual trees that are too deep can still overfit local data artifacts. Limiting max depth (e.g., to 10–20 levels) acts as a pre-pruning technique, while other hyperparameters like min_samples_split or min_samples_leaf provide post-pruning control. In practice, tuning max_depth alongside min_samples_leaf is a common strategy to balance bias and variance in high-dimensional datasets.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Decrease the maximum depth of each tree — Decreasing the maximum depth of each tree limits the complexity of individual trees, preventing them from memorizing noise and outliers in the training data. This directly reduces overfitting by enforcing simpler decision boundaries, which is a core regularization technique for ensemble methods like Random Forest.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 30, 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.