AI Associate Ethical AI and Data Privacy Practice Question
A Salesforce admin wants to display an explanation for why a specific lead received a high score from Einstein Lead Scoring. Which Salesforce feature provides this transparency?
⚠ Common exam trap
Candidates often confuse the Einstein Trust Layer audit trail (which logs data access for compliance) with the Score Factors feature (which provides model explainability), leading them to pick D instead of A.
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
✓
Score Factors in Einstein Lead Scoring
Score Factors in Einstein Lead Scoring provides transparency by listing the specific data points (e.g., lead source, industry, engagement history) that contributed to a lead's score. This feature allows admins to see exactly why a lead received a high score, enabling them to validate or adjust the scoring model. It directly addresses the need for explainability in AI-driven lead scoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Score Factors in Einstein Lead Scoring
Why this is correct
Score Factors display the top contributing fields and their impact on the lead score.
- ✗
Einstein Activity Capture
Why it's wrong here
Activity Capture logs emails and events, not prediction explanations.
- ✗
Einstein Copilot prompt template
Why it's wrong here
Prompt templates are for conversational AI, not lead scoring explanations.
- ✗
Einstein Trust Layer audit trail
Why it's wrong here
The audit trail logs AI actions but does not provide per-prediction explanations.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.