AI Associate AI Fundamentals Practice Question
Which type of machine learning is used when a model is trained on historical sales data that includes both input features and the known outcome (e.g., closed won/lost) to predict whether a new lead will convert?
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 training data where the correct output is provided. Lead scoring with historical outcomes is a classic supervised learning task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Semi-supervised learning
Why it's wrong here
Semi-supervised learning uses a small amount of labeled data and a larger unlabeled set; not the standard approach for lead scoring.
- ✓
Supervised learning
Why this is correct
Correct: supervised learning uses labeled outcomes to train a predictive model.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning learns from rewards/punishments in an environment, not from static historical data.
- ✗
Unsupervised learning
Why it's wrong here
Unsupervised learning finds patterns in unlabeled data; lead scoring requires known outcomes.
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