AI0-001 AI Concepts and Techniques Practice Question
A data scientist is building a model to predict credit default using historical loan data. The dataset contains 100,000 records with 50 features, including income, debt-to-income ratio, and loan amount. The target variable is binary (default vs. no default). The goal is to maximize interpretability while maintaining high accuracy. Which algorithm is MOST appropriate?
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
✓
Logistic regression
Logistic regression is interpretable (coefficients show feature impact) and performs well on binary classification with a large dataset. Decision trees are interpretable but may overfit; random forests and gradient boosting are less interpretable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Logistic regression
Why this is correct
Logistic regression provides clear odds ratios and feature coefficients, making it highly interpretable, and it performs well on large datasets with moderate feature complexity.
- ✗
Random forest
Why it's wrong here
Random forest is an ensemble method that sacrifices interpretability for performance; not the best choice when interpretability is a top priority.
- ✗
Gradient boosting machine
Why it's wrong here
Gradient boosting is powerful but complex and difficult to interpret; also prone to overfitting without careful tuning.
- ✗
Decision tree
Why it's wrong here
Decision trees are interpretable but often overfit and may not achieve high accuracy on complex data without pruning.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 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 AI0-001 exam.