DA0-002 Data Analysis Practice Question
A data scientist is tuning a decision tree model to prevent overfitting. The model currently has a high variance. Which hyperparameter adjustment is most effective?
⚠ Common exam trap
CompTIA often tests the misconception that increasing model complexity (e.g., more leaves) reduces overfitting, when in reality it increases variance; the trap here is that candidates may confuse 'minimum samples split' as the only regularization technique, overlooking that reducing max depth is a more direct and effective hyperparameter for high variance.
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 maximum depth
Reducing maximum depth limits the number of splits in the decision tree, which directly reduces model complexity and variance. A high-variance model is overfitting to training data, and capping depth prevents the tree from learning overly specific patterns that do not generalize.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce maximum depth
Why this is correct
Reducing max depth stops the tree from growing too deep, simplifying the model and reducing variance.
- ✗
Increase minimum samples split
Why it's wrong here
Increasing min samples split can help but is less direct than reducing depth.
- ✗
Increase number of leaves
Why it's wrong here
Increasing leaves adds complexity, increasing variance.
- ✗
Use a smaller dataset
Why it's wrong here
Using a smaller dataset typically increases variance, not reduces it.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 practice question is part of Courseiva's free CompTIA 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 DA0-002 exam.