AI Associate AI Fundamentals Practice Question
A financial services company is deploying Einstein AI and must comply with regulations requiring explainable decisions. Which Einstein capability allows them to understand why an AI model made a specific prediction?
⚠ Common exam trap
Salesforce often tests the distinction between data security features (like Data Mask or Shield) and AI explainability features, so candidates mistakenly choose a security-focused option when the question explicitly asks about understanding model predictions.
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
✓
Einstein Trust Layer with model explainability features.
The Einstein Trust Layer includes model explainability features that provide insights into why a specific prediction was made, such as highlighting the key input features that influenced the outcome. This directly addresses regulatory requirements for explainable AI decisions by offering transparency into the model's reasoning process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Einstein Trust Layer with model explainability features.
Why this is correct
The Trust Layer includes capabilities to explain predictions.
- ✗
Data Mask to protect sensitive information.
Why it's wrong here
Data Mask protects privacy, not explainability.
- ✗
Salesforce Shield with encryption and monitoring.
Why it's wrong here
Shield provides security, not explanation of AI decisions.
- ✗
Field Audit Trail to track changes to data.
Why it's wrong here
Audit trail tracks data changes, not model reasoning.
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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.