MLS-C01 Modeling Practice Question
Which TWO techniques can help reduce overfitting in a decision tree model?
⚠ Common exam trap
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.
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
✓
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.
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 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.
- ✓
Prune the tree after training
Why this is correct
Pruning reduces complexity.
- ✗
Increase the maximum depth of the tree
Why it's wrong here
Deeper trees overfit more.
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.