AI Associate AI Fundamentals Practice Question
A sales director wants to implement lead scoring but has no historical data on which leads converted. What approach can the team use to start?
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
✓
Use unsupervised learning to cluster leads into segments and score based on cluster characteristics
Unsupervised learning can cluster leads based on similarities, providing initial scores without labeled outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use unsupervised learning to cluster leads into segments and score based on cluster characteristics
Why this is correct
Correct. Clustering reveals patterns; leads in high-value clusters get higher scores.
- ✗
Train a supervised model using assumptions as labels
Why it's wrong here
Assumptions may introduce bias; unsupervised is safer.
- ✗
Skip lead scoring until enough conversion data is collected
Why it's wrong here
Business needs often require immediate solutions; unsupervised provides a starting point.
- ✗
Use reinforcement learning to learn scoring from sales team feedback
Why it's wrong here
Reinforcement learning requires an interactive environment; not practical for initial scoring.
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