20+ practice questions focused on Ethical AI and Data Privacy — one of the most tested topics on the Salesforce AI Associate AI Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Ethical AI and Data Privacy PracticeA sales operations manager notices that the AI-driven lead scoring model assigns lower scores to leads from a particular region, even though those leads historically convert at a higher rate. Which Salesforce Trusted AI principle is most directly violated?
Explanation: The AI-driven lead scoring model is producing outputs that do not match the ground truth (historical conversion rates), which is a direct failure of the Accuracy principle. Accuracy requires that AI systems perform as intended and produce reliable, correct predictions; here, the model's scores are systematically wrong for a specific region, violating that requirement.
A company deploys an AI-powered email composer that drafts responses to customer inquiries. To comply with GDPR, which control should they implement regarding automated decisions?
Explanation: Under GDPR, Article 22 grants individuals the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For an AI email composer that drafts responses to customer inquiries, requiring human review before any AI-generated email is sent ensures that the final decision to communicate is not fully automated, thereby complying with GDPR's requirement for meaningful human intervention in automated decision-making.
What is the purpose of ‘toxicity detection’ in the Einstein Trust Layer?
Explanation: Toxicity detection in the Einstein Trust Layer is designed to identify and filter harmful or abusive language in AI-generated outputs before they reach the user. It uses natural language processing (NLP) models to score content against categories such as hate speech, profanity, and harassment, ensuring outputs remain safe and appropriate. This prevents the AI from inadvertently disseminating offensive material, which is critical for maintaining trust and compliance in enterprise environments.
A data scientist is building a churn prediction model for a subscription service. The dataset includes highly correlated features: ‘number of support tickets’ and ‘average response time’. Which action is BEST to ensure model accuracy and interpretability?
Explanation: Removing one of the highly correlated features (D) is the best action because multicollinearity between 'number of support tickets' and 'average response time' can inflate the variance of coefficient estimates, making the model unstable and harder to interpret. By dropping one feature, you reduce redundancy without significant information loss, preserving both accuracy and interpretability in a linear or tree-based model.
When using Einstein Copilot to generate email content, what mechanism ensures that the AI does not use customer data to improve the underlying large language model?
Explanation: Einstein Copilot employs a zero data retention policy specifically for the underlying large language model (LLM). This means that any customer data processed during email generation is not stored, logged, or used for model training or fine-tuning, ensuring compliance with data privacy standards. The mechanism explicitly prevents the LLM from learning from or being improved by customer interactions, isolating the AI's behavior from proprietary data.
+15 more Ethical AI and Data Privacy questions available
Practice all Ethical AI and Data Privacy questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Ethical AI and Data Privacy. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Ethical AI and Data Privacy questions on the AI Associate frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Ethical AI and Data Privacy is tested as part of the Salesforce AI Associate AI Associate blueprint. Practicing with targeted Ethical AI and Data Privacy questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Ethical AI and Data Privacy is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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