AI Associate AI Fundamentals Practice Question
A sales team is implementing Einstein Lead Scoring. Which two actions should they take to ensure the model is effective? (Choose 2)
⚠ Common exam trap
Salesforce often tests the misconception that Einstein Lead Scoring can work effectively with only positive examples (converted leads), but the model fundamentally requires both positive and negative examples to learn the difference between high- and low-quality leads.
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
✓
Ensure the training data includes both converted and unconverted leads.
Einstein Lead Scoring requires training data that includes both converted and unconverted leads to build a predictive model that can distinguish between leads likely to convert and those that are not. Without unconverted leads, the model cannot learn the negative patterns, leading to biased predictions and poor accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Include only demographic data for scoring.
Why it's wrong here
Demographic only is insufficient.
- ✗
Set a fixed score threshold for all users.
Why it's wrong here
Threshold should be adjustable.
- ✗
Disable the model for low-volume leads.
Why it's wrong here
Disabling reduces coverage.
- ✓
Ensure the training data includes both converted and unconverted leads.
Why this is correct
Balanced data is necessary.
- ✓
Regularly review and provide feedback on lead conversions.
Why this is correct
Feedback retrains the model.
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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.