AI Associate AI Fundamentals Practice Question
A retailer wants to recommend products to customers based on their purchase history and browsing behavior. Which AI approach is most suitable?
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 predict purchase probability for each product
Product recommendations are typically handled by supervised or unsupervised learning, but the most common approach in CRM is collaborative filtering or similar supervised/unsupervised models. However, given options, supervised learning on historical interactions is typical.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Supervised learning to predict purchase probability for each product
Why this is correct
Supervised learning can be trained on historical purchases to predict which products a customer is likely to buy.
- ✗
Reinforcement learning with real-time rewards
Why it's wrong here
Reinforcement learning is overkill and rarely used for straightforward product recommendations.
- ✗
Natural language generation to create product descriptions
Why it's wrong here
NLG generates text, not recommendations.
- ✗
Computer vision to analyze product images
Why it's wrong here
Computer vision is not directly used for recommending products based on user behavior.
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This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.