MLS-C01 Modeling Practice Question
A data scientist is training a random forest model for regression. The model shows high variance on the validation set. Which TWO actions are most likely to reduce variance? (Choose 2.)
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that adding more trees always reduces variance, but the trap here is that while more trees reduce variance from averaging, they do not address the root cause of overfitting from overly complex individual trees.
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
✓
Decrease the maximum depth of trees
Both decreasing the maximum depth of trees (B) and increasing the minimum samples per leaf (C) reduce the complexity of individual trees. Decreasing max depth limits tree growth, preventing overfitting to noise. Increasing min samples per leaf forces leaves to contain more samples, smoothing predictions and reducing variance. Together, these regularization techniques directly combat high variance in random forest models.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use bootstrap sampling with replacement
Why it's wrong here
Bootstrap is inherent to random forest and not a variance reduction technique.
- ✓
Decrease the maximum depth of trees
Why this is correct
Shallow trees reduce overfitting, lowering variance.
- ✓
Increase the minimum samples per leaf
Why this is correct
Larger leaf samples reduce model complexity, reducing variance.
- ✗
Increase the number of trees in the forest
Why it's wrong here
More trees typically stabilize predictions, reducing variance.
- ✗
Increase the number of features considered at each split
Why it's wrong here
More features can increase variance.
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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.