- A
Increase the max_samples parameter.
Why wrong: Increasing max_samples may actually increase the variance of individual trees, potentially worsening overfitting, and is not a standard method to reduce overfitting.
- B
Reduce the max_depth of the trees.
Correct. Reducing max_depth limits tree depth, reducing model complexity and overfitting.
- C
Increase the max_features parameter.
Why wrong: Increasing max_features can increase diversity among trees, but it does not directly reduce overfitting and may sometimes increase it by allowing more complex trees.
- D
Increase the number of trees (n_estimators).
Why wrong: Increasing the number of trees (n_estimators) reduces variance and can help generalization, but it is less direct than limiting tree complexity. However, the question expects the two most direct methods: reduce max_depth and increase min_samples_leaf.
- E
Increase the min_samples_leaf parameter.
Correct. Increasing min_samples_leaf forces leaves to have more samples, smoothing the model and reducing overfitting.
MLS-C01 Overfitting 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. A key principle to apply: overfitting. 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?
Answer choices
Why each option matters
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, as indicated by high training accuracy but poor test accuracy. To improve generalization, reduce model complexity. Reducing max_depth (B) limits the depth of each tree, preventing overly specific splits. Increasing min_samples_leaf (E) requires a minimum number of samples per leaf, which smooths the model and reduces variance. These two actions directly combat overfitting in random forests.
Key principle: Overfitting
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 may actually increase the variance of individual trees, potentially worsening overfitting, and is not a standard method to reduce overfitting.
- ✓
Reduce the max_depth of the trees.
Why this is correct
Correct. Reducing max_depth limits tree depth, reducing model complexity and overfitting.
Related concept
Overfitting
- ✗
Increase the max_features parameter.
Why it's wrong here
Increasing max_features can increase diversity among trees, but it does not directly reduce overfitting and may sometimes increase it by allowing more complex trees.
- ✗
Increase the number of trees (n_estimators).
Why it's wrong here
Increasing the number of trees (n_estimators) reduces variance and can help generalization, but it is less direct than limiting tree complexity. However, the question expects the two most direct methods: reduce max_depth and increase min_samples_leaf.
- ✓
Increase the min_samples_leaf parameter.
Why this is correct
Correct. Increasing min_samples_leaf forces leaves to have more samples, smoothing the model and reducing overfitting.
Related concept
Overfitting
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Overfitting
- max_depth
- min_samples_leaf
TExam Day Tips
- 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
Overfitting
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. Overfitting 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.
Review overfitting, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Overfitting.
What is the correct answer to this question?
The correct answer is: Reduce the max_depth of the trees. — The model is overfitting, as indicated by high training accuracy but poor test accuracy. To improve generalization, reduce model complexity. Reducing max_depth (B) limits the depth of each tree, preventing overly specific splits. Increasing min_samples_leaf (E) requires a minimum number of samples per leaf, which smooths the model and reduces variance. These two actions directly combat overfitting in random forests.
What should I do if I get this MLS-C01 question wrong?
Review overfitting, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Overfitting
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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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