Quick Answer
The answer is to prune the tree after training and limit the maximum depth. These two techniques directly combat overfitting by controlling model complexity: limiting max depth restricts the number of splits, preventing the tree from memorizing noise, while post-training pruning removes branches that offer little predictive power, reducing variance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of bias-variance tradeoff and regularization in tree-based models. A common trap is confusing pre-pruning (like setting max depth) with post-pruning, or assuming more depth always improves accuracy. Remember the mnemonic “Depth and Prune, Overfit to Tune”—controlling depth during training and pruning afterward are your two levers for generalization.
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.
Which TWO techniques can help reduce overfitting in a decision tree model?
Increase the number of trees in the forest
Why wrong: Not applicable to a single decision tree.
Increase the number of features considered per split
Why wrong: More features can lead to overfitting.
Limit the maximum depth of the tree
Shallower trees generalize better.
Prune the tree after training
Pruning reduces complexity.
Increase the maximum depth of the tree
Why wrong: Deeper trees overfit more.
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 the tree
Limiting the maximum depth of the tree (Option C) directly restricts the number of splits, preventing the model from learning overly specific patterns in the training data. Pruning the tree after training (Option D) removes branches that have little predictive power, reducing variance and improving generalization. Both techniques combat overfitting by controlling the complexity of the decision tree.
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 in the forest
Why it's wrong here
Not applicable to a single decision tree.
Increase the number of features considered per split
Why it's wrong here
More features can lead to overfitting.
Limit the maximum depth of the tree
Why this is correct
Shallower trees generalize better.
Related concept
Read the scenario before looking for a memorised answer.
Prune the tree after training
Why this is correct
Pruning reduces complexity.
Related concept
Read the scenario before looking for a memorised answer.
Increase the maximum depth of the tree
Why it's wrong here
Deeper trees overfit more.
Common exam traps
AWS often tests the distinction between techniques that reduce overfitting in a single decision tree versus ensemble methods, so candidates mistakenly apply Random Forest concepts (like increasing trees or features) to a standalone tree.
Detailed technical explanation
Decision trees are prone to overfitting because they can split until each leaf contains a single instance, memorizing the training set. Limiting depth (e.g., max_depth=5) acts as a pre-pruning hyperparameter, while post-pruning (e.g., cost-complexity pruning using ccp_alpha) removes subtrees that increase the validation error. In practice, cross-validation is used to tune these parameters, balancing bias and variance.
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
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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 the tree — Limiting the maximum depth of the tree (Option C) directly restricts the number of splits, preventing the model from learning overly specific patterns in the training data. Pruning the tree after training (Option D) removes branches that have little predictive power, reducing variance and improving generalization. Both techniques combat overfitting by controlling the complexity of the decision tree.
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.
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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.
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