AI Associate AI Fundamentals Practice Question
A sales team wants to prioritize leads that are most likely to convert. They have historical data on lead attributes and conversion outcomes. Which AI technique 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 to build a lead scoring model
Lead scoring uses supervised learning on historical lead data to predict conversion probability.
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 clustering to group leads by similarity
Why it's wrong here
Clustering groups leads without predicting conversion; it doesn't provide a score for likelihood to convert.
- ✓
Supervised learning to build a lead scoring model
Why this is correct
Supervised learning uses labeled historical data to predict a target outcome, perfect for lead scoring.
- ✗
Natural language processing to analyze lead emails
Why it's wrong here
NLP focuses on text analysis, not numerical prediction of conversion likelihood from structured data.
- ✗
Computer vision to analyze lead profile pictures
Why it's wrong here
Computer vision is irrelevant to predicting lead conversion from tabular data.
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