AI Associate AI Fundamentals Practice Question
A sales operations manager wants to predict which leads are most likely to convert to deals. The CRM has historical data on thousands of leads with outcomes (converted or not). Which type of machine learning should they use?
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 (past leads with known outcomes) to predict future lead conversion. Unsupervised learning finds patterns without labels, reinforcement learning learns from rewards, and deep learning is a subset of supervised/unsupervised but not the most specific answer here.
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 outcome labels and cannot directly predict conversion probability.
- ✓
Supervised learning
Why this is correct
Supervised learning trains on labeled historical data (features + outcome) to predict future outcomes like lead conversion.
- ✗
Deep learning
Why it's wrong here
Deep learning is a subfield of machine learning; while it can be used, supervised learning is the broader correct category here.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning is used for sequential decision-making with rewards, not for static lead scoring.
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