Quick Answer
The answer is to reduce maximum depth. This hyperparameter adjustment is most effective because it directly limits the number of splits the tree can make, capping its complexity and reducing variance. A high-variance model is overfitting by memorizing noise and specific patterns in the training data, and reducing maximum depth forces the tree to stop splitting earlier, promoting simpler, more generalizable rules. On the CompTIA Data+ DA0-001 exam, this question tests your understanding of the bias-variance tradeoff and how hyperparameter tuning directly addresses overfitting. A common trap is confusing maximum depth with minimum samples per leaf—both reduce complexity, but depth is the most direct lever for controlling split count. Remember the memory tip: “Deep trees dig too deep; shallow trees generalize.”
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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.
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?
Reduce maximum depth
Reducing max depth stops the tree from growing too deep, simplifying the model and reducing variance.
Increase minimum samples split
Why wrong: Increasing min samples split can help but is less direct than reducing depth.
Increase number of leaves
Why wrong: Increasing leaves adds complexity, increasing variance.
Use a smaller dataset
Why wrong: Using a smaller dataset typically increases variance, not reduces it.
Answer choices
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.
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.
Reduce maximum depth
Why this is correct
Reducing max depth stops the tree from growing too deep, simplifying the model and reducing variance.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
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.
Detailed technical explanation
Decision trees split nodes based on impurity measures like Gini impurity or entropy; each split increases depth and captures finer-grained patterns. Reducing max depth effectively prunes the tree at a global level, while minimum samples split only affects nodes that would otherwise split with few samples. In practice, tuning max depth is often the first step to combat overfitting because it directly limits the hypothesis space, whereas minimum samples split can still allow deep trees if many samples exist at each node.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
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 DA0-001 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 DA0-001 exam.
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