- A
Score Factors in Einstein Lead Scoring
Score Factors display the top contributing fields and their impact on the lead score.
- B
Einstein Activity Capture
Why wrong: Activity Capture logs emails and events, not prediction explanations.
- C
Einstein Copilot prompt template
Why wrong: Prompt templates are for conversational AI, not lead scoring explanations.
- D
Einstein Trust Layer audit trail
Why wrong: The audit trail logs AI actions but does not provide per-prediction explanations.
Question 507 of 1,000
Using Score Factors for Lead Scoring Explainability
This AI Associate practice question tests your understanding of ethical ai and data privacy. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, Einstein Lead Scoring uses a gradient-boosted tree model trained on historical lead conversion data. Score Factors are derived from feature importance calculations (e.g., SHAP values) that quantify how each attribute (e.g., 'Industry = Technology') influenced the predicted probability. In a real-world scenario, if a lead from a high-conversion industry suddenly scores low, an admin can inspect Score Factors to see that a negative factor like 'No email engagement' outweighed the positive industry signal.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Ethical AI and Data Privacy — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Score Factors in Einstein Lead Scoring — Option A is correct because 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A sales manager wants to use Einstein Lead Scoring but is concerned about transparency for the sales team. Which TWO features should they enable to provide explainability? (Choose 2)
easy- A.Einstein Copilot
- ✓ B.Score Factors on the lead record
- C.Einstein Activity Capture
- ✓ D.A custom field indicating the score is AI-generated
- E.Einstein Trust Layer audit trail
Why B: Score Factors show why a lead scored as it did, and labeling AI-generated scores helps reps understand that the score is AI-driven.
Variation 2. When a Salesforce admin enables 'Score Factors' for an AI prediction, what does this provide to end users?
easy- A.The exact formula used by the model
- B.A confidence interval for the prediction
- C.A histogram of all prediction values
- ✓ D.A list of the most influential fields and their contribution to the prediction
Why D: When 'Score Factors' is enabled for an AI prediction in Salesforce, end users see a breakdown of the most influential fields that contributed to the prediction, along with their relative contribution (e.g., positive or negative impact). This provides transparency into why a specific prediction was made, helping users trust and act on the AI's output without exposing the underlying model logic.
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Last reviewed: Jul 4, 2026
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.
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