AI0-001 AI Concepts and Techniques Practice Question
A data scientist is building a model to predict whether a credit card transaction is fraudulent, using labeled historical data. Which machine learning paradigm is being used?
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
✓
Supervised learning
Supervised learning uses labeled data to train a model to map inputs to outputs. Fraud detection with historical labels is a classic binary classification problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses rewards/penalties from interactions; this is not the case here.
- ✗
Unsupervised learning
Why it's wrong here
Unsupervised learning works with unlabeled data; here labels are available.
- ✓
Supervised learning
Why this is correct
The model is trained on labeled data (fraud vs. legitimate) to predict outcomes, which is supervised learning.
- ✗
Self-supervised learning
Why it's wrong here
Self-supervised learning creates labels from data itself; here labels are provided externally.
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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.