AI0-001 AI Concepts and Techniques Practice Question
A data analyst wants to use a model that provides feature importance scores to understand which factors most influence customer churn. They also need the model to handle both numerical and categorical data with minimal preprocessing. Which algorithm is BEST suited?
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
✓
Random forest
Random forests provide feature importance, handle mixed data types, and require little preprocessing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Random forest
Why this is correct
Random forests output feature importance, handle mixed data, and are robust to scaling.
- ✗
Support vector machine (SVM) with RBF kernel
Why it's wrong here
SVMs do not natively provide feature importance and require feature scaling.
- ✗
Logistic regression
Why it's wrong here
Logistic regression coefficients can indicate direction but not direct importance, and requires encoding categorical variables.
- ✗
k-nearest neighbours (k-NN)
Why it's wrong here
k-NN is sensitive to scaling and provides no feature importance.
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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.