AI Associate Ethical AI and Data Privacy Practice Question
A company uses Einstein Discovery to analyze sales data and provide recommendations. A sales rep wants to understand why a specific opportunity was predicted to close. Which Einstein feature should the rep use?
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
✓
The 'Score Factors' or 'Prediction Explanation' component available on the record page.
Einstein Discovery provides 'score factors' that show the most influential fields and their impact on the prediction, enabling transparency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The 'Model Performance' tab in Einstein Discovery Settings.
Why it's wrong here
Model Performance shows aggregate metrics, not individual explanations.
- ✗
The 'Prediction Summary' report in Einstein Analytics.
Why it's wrong here
Prediction Summary might show overall model performance, not per-opportunity explanations.
- ✓
The 'Score Factors' or 'Prediction Explanation' component available on the record page.
Why this is correct
Score Factors list the key drivers for that specific prediction, offering explainability.
- ✗
The 'Data Quality' dashboard to check if the prediction is reliable.
Why it's wrong here
Data quality is important but does not explain a single prediction.
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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.