AI Associate Ethical AI and Data Privacy Practice Question
A financial services company is deploying an Einstein chatbot that provides investment advice. They want to ensure that if the chatbot generates a potentially harmful recommendation (e.g., suggesting a risky trade), the message is blocked before reaching the customer. Which Einstein Trust Layer capability should they rely on?
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
✓
Toxicity detection
Toxicity detection identifies harmful or offensive content. In this context, harmful financial advice can be flagged as toxic. Human oversight (agent review) would also be applicable, but the question specifically asks about blocking before reaching the customer via a capability of the Trust Layer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Toxicity detection
Why this is correct
Toxicity detection can identify harmful content; the Trust Layer can be configured to block such outputs before delivery.
- ✗
Grounding
Why it's wrong here
Grounding connects AI responses to CRM data to ensure relevance, but does not block harmful advice.
- ✗
Zero data retention
Why it's wrong here
Zero data retention ensures customer data is not stored, but does not filter output content.
- ✗
PII masking
Why it's wrong here
PII masking removes personally identifiable information, but does not detect harmful advice.
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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.