AI Associate AI Fundamentals Practice Question
A company wants to use AI to reduce customer churn. Which TWO approaches are most appropriate? (Select 2)
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 predictive AI to score churn risk based on historical data
Predictive AI can forecast churn likelihood, and sentiment analysis can identify dissatisfied customers early.
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 predictive AI to score churn risk based on historical data
Why this is correct
Predictive models can estimate churn probability from historical patterns.
- ✗
Use generative AI to create personalized retention offers
Why it's wrong here
Generative AI can create offers, but churn reduction typically starts with prediction and detection.
- ✗
Use computer vision to analyze customer photos
Why it's wrong here
Computer vision is unrelated to churn prediction.
- ✗
Use reinforcement learning to train a chatbot
Why it's wrong here
Reinforcement learning is not directly used for churn prediction.
- ✓
Use sentiment analysis on customer support interactions to detect dissatisfaction
Why this is correct
Sentiment analysis can flag unhappy customers for proactive retention.
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