AI0-001 Machine Learning and Deep Learning Practice Question
Which TWO are characteristics of supervised learning?
⚠ Common exam trap
The AI0-001 exam often tests the distinction between supervised and unsupervised learning by presenting 'clustering' or 'reinforcement signals' as plausible characteristics of supervised learning, trapping candidates who confuse task types.
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
✓
Requires labeled data
Supervised learning requires labeled data because the model learns a mapping from input features to a known target variable. The correct answer B is fundamental: without labeled examples, the algorithm cannot calculate a loss function to adjust its weights during training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Does not require target variable
Why it's wrong here
Without targets, it would be unsupervised.
- ✓
Requires labeled data
Why this is correct
Supervised learning uses input-output pairs for training.
- ✗
Uses reinforcement signals
Why it's wrong here
Reinforcement learning uses rewards, not labeled data.
- ✗
Learns to cluster data
Why it's wrong here
Clustering is unsupervised.
- ✓
Predicts continuous or categorical output
Why this is correct
Supervised learning includes regression and classification.
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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.