AI0-001 AI Concepts and Techniques Practice Question
A data scientist is selecting a model for a binary classification task where interpretability is critical because of regulatory requirements. The dataset has 20 features and 10,000 samples. Which model 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
✓
Decision tree
Decision trees are inherently interpretable, showing the decision rules. Random forests and gradient boosting are ensembles that sacrifice interpretability for accuracy. Neural networks are black-box 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.
- ✗
Neural network (MLP)
Why it's wrong here
Neural networks are black-box models with low interpretability.
- ✓
Decision tree
Why this is correct
A single decision tree provides clear, human-readable decision rules, meeting regulatory interpretability needs.
- ✗
Gradient boosting machine
Why it's wrong here
GBMs are powerful but not easily interpretable.
- ✗
Random forest classifier
Why it's wrong here
Random forests are less interpretable than a single decision tree.
About these practice questions
This AI0-001 question is part of Courseiva's 754-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.