The answer is increasing the minimum samples per leaf. This hyperparameter adjustment directly combats overfitting by preventing the decision tree from creating leaf nodes that capture noise or outliers in the training data, forcing each leaf to represent a more general pattern based on a larger sample size. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of the bias-variance tradeoff: a depth-20 tree with perfect training accuracy but poor test accuracy is a classic sign of high variance, and increasing the minimum samples per leaf simplifies the model to improve generalization. A common trap is to instead increase tree depth or reduce pruning, which would worsen overfitting. Remember the memory tip: “More leaves, more grief; more samples, more stable.”
DA0-001 Analyzing and Modeling Data Practice Question
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.
After training a decision tree, the tree has depth 20 and 100% accuracy on training data but only 60% on test data. Which hyperparameter adjustment is most likely to improve generalization?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase minimum samples per leaf
The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 number of estimators
Why it's wrong here
Number of estimators is a parameter for ensemble methods like Random Forest, not for a single decision tree.
✗
Decrease minimum samples per split
Why it's wrong here
Decreasing min_samples_split allows splits on smaller samples, increasing complexity and overfitting.
✓
Increase minimum samples per leaf
Why this is correct
Increasing min_samples_leaf prevents the tree from fitting noise by requiring more samples in each leaf, reducing overfitting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase maximum depth
Why it's wrong here
Increasing max_depth makes the tree deeper and more prone to overfitting, not less.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse hyperparameters that reduce overfitting with those that increase model complexity, mistakenly choosing options like 'increase maximum depth' or 'decrease minimum samples per split' thinking they will improve accuracy.
Detailed technical explanation
How to think about this question
Decision trees split nodes to minimize impurity (e.g., Gini impurity or entropy). With a depth of 20, the tree can memorize noise and outliers in the training data. Increasing min_samples_leaf (e.g., from 1 to 10 or 20) acts as a regularization parameter, forcing the tree to average more instances per leaf, which smooths the decision boundary. In practice, cross-validation is used to tune this hyperparameter to balance bias and variance.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase minimum samples per leaf — The model is overfitting: 100% training accuracy vs. 60% test accuracy with a depth-20 tree. Increasing minimum samples per leaf forces the tree to be simpler by requiring more samples in each leaf, reducing variance and improving generalization. This directly combats the overfitting caused by the overly deep tree.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. What is the most likely issue?
hard
A.Underfitting
✓ B.Overfitting
C.Multicollinearity
D.High bias
Why B: The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.
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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.
Question Discussion
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