AI Associate AI Fundamentals Practice Question
A sales director wants to use AI to prioritize leads that are most likely to convert. The company has historical data on leads that includes whether they converted (yes/no) and various attributes. Which machine learning type should be 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 (classification)
Lead scoring is a binary classification problem (convert or not) using historical labeled data, which is supervised learning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Generative AI
Why it's wrong here
Generative AI creates new data, not predicts binary outcomes.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning learns from sequential actions and rewards; not suitable for static lead data.
- ✗
Unsupervised learning (clustering)
Why it's wrong here
Clustering groups leads without using conversion labels; cannot directly predict conversion.
- ✓
Supervised learning (classification)
Why this is correct
Correct: supervised classification uses labeled conversion outcomes to predict new leads.
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