AI0-001 AI Concepts and Techniques Practice Question
Which machine learning paradigm is best suited for training a model to play a game by learning from its own actions and rewards, without labeled data?
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
✓
Reinforcement learning
Reinforcement learning learns via trial-and-error using rewards and penalties, ideal for game-playing agents. Supervised learning requires labeled data; unsupervised learning finds patterns without rewards; semi-supervised uses a mix.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Unsupervised learning
Why it's wrong here
Unsupervised learning does not use rewards or target outcomes, so it cannot learn to maximize game score.
- ✗
Semi-supervised learning
Why it's wrong here
Semi-supervised learning still requires some labeled data and does not inherently use a reward signal.
- ✓
Reinforcement learning
Why this is correct
Reinforcement learning uses rewards from the environment to learn optimal actions through exploration and exploitation.
- ✗
Supervised learning
Why it's wrong here
Supervised learning needs labeled examples of optimal moves, which are often unavailable for complex games.
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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.