AI Associate AI Capabilities in CRM Practice Question
Which THREE factors influence the prediction accuracy of Einstein Lead Scoring?
⚠ Common exam trap
Salesforce often tests the misconception that any lead-related data, such as custom formula fields or rep viewing counts, directly influences Einstein Lead Scoring, when in fact only historical conversion data and patterns from standard fields like industry and lead source are 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
✓
Historical conversion data of leads
Einstein Lead Scoring relies on historical conversion data to identify patterns that distinguish leads likely to convert. By analyzing past leads that converted, the model learns which attributes and behaviors correlate with successful outcomes, directly influencing prediction 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.
- ✗
Custom formula fields on the lead object
Why it's wrong here
Einstein uses standard fields, not formula fields.
- ✗
Number of times a lead is viewed by sales reps
Why it's wrong here
User activity is not a model input.
- ✓
Historical conversion data of leads
Why this is correct
The model learns from past conversions.
- ✓
Conversion patterns across different lead sources
Why this is correct
Source is a key predictor in the model.
- ✓
Values in standard lead fields like industry and company size
Why this is correct
Field values are used as predictors.
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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.