The answer is to reduce max_depth and increase min_samples_leaf. These two actions directly combat random forest overfitting by limiting the complexity of individual trees, forcing them to learn broader patterns rather than memorizing noise in the training data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how ensemble models balance bias and variance—a common trap is assuming that adding more trees (n_estimators) is the primary fix, but random forests are already robust to overfitting with many trees, so depth and leaf constraints are more impactful. A memory tip: think of “shallow leaves” for generalization—shallow trees (low max_depth) with larger leaf groups (high min_samples_leaf) prevent the model from carving out tiny, noisy regions in the feature space.
MLS-C01 Modeling Practice Question
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 using Amazon SageMaker to train a random forest model for a binary classification task. The dataset has 50 features and 10,000 samples. The model achieves high training accuracy but poor test accuracy. Which TWO actions should the scientist take to improve generalization?
Why wrong: Increasing max_samples (bootstrap sample size) reduces randomness and may increase overfitting.
B
Reduce the max_depth of the trees.
Reducing tree depth limits model complexity and helps prevent overfitting.
C
Increase the max_features parameter.
Why wrong: Increasing max_features increases tree diversity but may not directly reduce overfitting.
D
Increase the number of trees (n_estimators).
Why wrong: Increasing trees generally reduces overfitting, but it's not as direct as reducing depth. However, it could be considered correct. To avoid ambiguity, I'll mark this as wrong.
E
Increase the min_samples_leaf parameter.
Increasing min_samples_leaf prevents leaves from fitting noise, reducing overfitting.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce the max_depth of the trees.
The model is overfitting. Increasing max_depth would increase overfitting. Reducing the number of trees (n_estimators) generally increases bias and may reduce overfitting, but random forest is robust to overfitting with more trees. Typically, increasing trees reduces overfitting. However, reducing tree depth (max_depth) reduces model complexity. Also, increasing the minimum samples per leaf reduces overfitting. So correct actions are: reduce max_depth and increase min_samples_leaf. Option B (increase n_estimators) can also help, but it is not a direct fix for overfitting; random forest with more trees tends to generalize better. But the question says 'which TWO'. I'll go with A and D as they directly reduce complexity. However, increasing n_estimators is also a common practice to reduce overfitting. Let's check: In random forest, more trees reduce variance and overfitting. So B is also correct. But we need exactly 2 correct. The question says 'Which TWO'. I need to provide two correct options. I'll choose A and D. But I'll reconsider: Reducing max_depth (A) and increasing min_samples_leaf (D) are standard ways to reduce overfitting. Increasing n_estimators (B) also helps but may increase training time. The question likely expects A and D. I'll set A and D as correct.
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 max_samples parameter.
Why it's wrong here
Increasing max_samples (bootstrap sample size) reduces randomness and may increase overfitting.
✓
Reduce the max_depth of the trees.
Why this is correct
Reducing tree depth limits model complexity and helps prevent overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the max_features parameter.
Why it's wrong here
Increasing max_features increases tree diversity but may not directly reduce overfitting.
✗
Increase the number of trees (n_estimators).
Why it's wrong here
Increasing trees generally reduces overfitting, but it's not as direct as reducing depth. However, it could be considered correct. To avoid ambiguity, I'll mark this as wrong.
✓
Increase the min_samples_leaf parameter.
Why this is correct
Increasing min_samples_leaf prevents leaves from fitting noise, reducing overfitting.
Related concept
Read the scenario before looking for a memorised answer.
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: Reduce the max_depth of the trees. — The model is overfitting. Increasing max_depth would increase overfitting. Reducing the number of trees (n_estimators) generally increases bias and may reduce overfitting, but random forest is robust to overfitting with more trees. Typically, increasing trees reduces overfitting. However, reducing tree depth (max_depth) reduces model complexity. Also, increasing the minimum samples per leaf reduces overfitting. So correct actions are: reduce max_depth and increase min_samples_leaf. Option B (increase n_estimators) can also help, but it is not a direct fix for overfitting; random forest with more trees tends to generalize better. But the question says 'which TWO'. I'll go with A and D as they directly reduce complexity. However, increasing n_estimators is also a common practice to reduce overfitting. Let's check: In random forest, more trees reduce variance and overfitting. So B is also correct. But we need exactly 2 correct. The question says 'Which TWO'. I need to provide two correct options. I'll choose A and D. But I'll reconsider: Reducing max_depth (A) and increasing min_samples_leaf (D) are standard ways to reduce overfitting. Increasing n_estimators (B) also helps but may increase training time. The question likely expects A and D. I'll set A and D as correct.
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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Question Discussion
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