AI Associate Ethical AI and Data Privacy — 20 Questions

Question 1

A 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?

Accepted Answer

Accuracy. 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.

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Empathy

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Safety

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Transparency

Question 2

What is the primary purpose of the Einstein Trust Layer in Salesforce's AI architecture?

Accepted Answer

To provide a secure gateway for AI data processing, including data masking and toxicity detection. The Einstein Trust Layer is designed to provide security, privacy, and governance controls for AI features within the Salesforce platform.

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To replace all third-party AI services with Salesforce-owned models

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To automatically generate AI models without any human oversight

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To train large language models on customer data for better predictions

Question 3

An organization using Einstein Prediction Builder wants to ensure that no customer personally identifiable information (PII) is used in model training. Which data governance practice should they enforce?

Accepted Answer

Data minimisation by selecting only non-PII fields as predictors. Option D is correct because the question specifically asks how to ensure no PII is used in model training. Data minimization by selecting only non-PII fields as predictors directly prevents PII from entering the training dataset at the source. This is a proactive governance practice that avoids reliance on post-processing or masking, which may still expose PII during intermediate steps.

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Enabling zero data retention in the Trust Layer

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Data anonymization via the Einstein Trust Layer

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Regularly auditing the model for bias

Question 4

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?

Accepted Answer

Require human review before any AI-generated email is sent. 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.

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Allow the AI to send emails automatically if confidence is high

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Disable the AI composer entirely to avoid GDPR risk

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Anonymize customer data before drafting

Question 5

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?

Accepted Answer

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.

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Einstein Activity Capture

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Einstein Copilot prompt template

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Einstein Trust Layer audit trail

Question 6

What is the purpose of ‘toxicity detection’ in the Einstein Trust Layer?

Accepted Answer

To detect and prevent harmful or abusive language in AI outputs. 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.

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To increase the speed of AI responses

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To monitor user interactions for compliance with data protection laws

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To identify and mask PII in user prompts

Question 7

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?

Accepted Answer

Remove one of the correlated features. 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.

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Keep both features to maximize information

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Apply principal component analysis to combine them

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Increase the regularization strength

Question 8

Under the Salesforce Data Processing Addendum (DPA), what is Salesforce's commitment regarding customer data used in AI services?

Accepted Answer

Customer data is not used to train or improve Salesforce's base AI models. Salesforce's DPA states that customer data will not be used to train or improve Salesforce's base AI models, ensuring customer data privacy.

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Customer data may be used to improve Salesforce's AI models unless the customer opts out

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Customer data is anonymized and then used to train public AI models

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Customer data is only used to train models for that specific customer

Question 9

Which Salesforce AI feature provides audit logging of when AI recommendations are generated and acted upon?

Accepted Answer

Einstein Trust Layer audit trail. Option A is correct because the Einstein Trust Layer includes an audit trail that logs when AI recommendations are generated and when users act on them. This audit trail is essential for compliance and governance, as it records the AI's decision-making process and user interactions, ensuring transparency and accountability in AI-driven recommendations.

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Einstein Discovery

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Einstein Copilot

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Einstein Prediction Builder

Question 10

A marketing manager wants to use Einstein to personalize email content for each customer. However, they are concerned about violating CCPA if they use certain data. Which data use would be MOST likely to raise a CCPA concern?

Accepted Answer

Customer location data from mobile devices. CCPA gives consumers the right to know what personal information is collected and used. Using purchase history and browsing behavior are typical for personalization, but using location data without explicit consent may be problematic.

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Customer purchase history from the past year

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Customer email address

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Customer browsing behavior on the company website

Question 11

An AI model predicts loan approvals, and the bank notices that the model disproportionately denies loans to a certain demographic group. Which combination of actions addresses the AI bias according to Salesforce's Trusted AI principles?

Accepted Answer

Audit the model for bias, provide transparency on decision factors, and require human review for denied applications. Option B is correct because it aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.

Answer

Disable the AI model and make all decisions manually

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Remove demographic data from the model entirely and continue using it

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Retrain the model with more data from the affected group and deploy automatically

Question 12

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?

Accepted Answer

Zero data retention. Option A is correct because 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.

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PII masking

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Grounding

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Toxicity detection

Question 13

A company is deploying an AI system to recommend products to customers. To comply with GDPR's right to explanation, which TWO practices should they implement? (Choose 2)

Accepted Answer

Provide a list of the most influential factors for each recommendation. Option B is correct because GDPR's right to explanation requires that individuals can understand the logic behind automated decisions. Providing a list of the most influential factors for each recommendation directly addresses this by offering transparency into the model's decision-making process, such as feature importance scores from a tree-based or linear model.

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Store all recommendation logs for at least 10 years

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Allow customers to opt out of all AI recommendations

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Use only anonymized data for recommendations

Question 14

A healthcare provider is using Einstein to predict patient readmission risks. They must ensure the model is both accurate and fair. Which THREE actions should they take? (Choose 3)

Accepted Answer

Validate model accuracy separately for different patient subgroups. Option B is correct because validating model accuracy separately for different patient subgroups is a key fairness practice. It ensures the model performs consistently across diverse populations, preventing hidden disparities that could lead to biased clinical decisions. This aligns with responsible AI principles that require subgroup performance analysis to detect and mitigate algorithmic bias.

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Use only historical data from the past six months

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Include as many features as possible to maximize predictive power

Question 15

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)

Accepted Answer

Score Factors on the lead record. Score Factors show why a lead scored as it did, and labeling AI-generated scores helps reps understand that the score is AI-driven.

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Einstein Copilot

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Einstein Activity Capture

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Einstein Trust Layer audit trail

Question 16

A sales rep receives an AI-generated lead score of 95, but the rep notices the lead's email domain is 'example.com' and the phone number is invalid. The rep suspects the AI model is overvaluing certain features. Which TWO actions should the rep take to investigate and address the issue?

Accepted Answer

Report the suspicious lead and request a model audit for bias. Option A is correct because reporting suspicious leads and requesting a model audit is the proper governance procedure when an AI model produces outputs that contradict observable data. This triggers a review of the model's feature weights and training data to identify bias or overvaluation of specific fields, ensuring the model remains reliable and compliant with ethical AI standards.

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Delete the lead from the CRM to avoid influencing future predictions

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Manually override the score to 50 and move on

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Contact Salesforce support to disable the AI model

Question 17

Which of the following is a key feature of Salesforce Einstein Trust Layer that protects customer data when using AI?

Accepted Answer

Zero data retention. Option A is correct because the Salesforce Einstein Trust Layer is designed with a zero data retention policy, meaning that once an AI model processes a request and returns a response, Salesforce does not store the customer's data. This ensures that sensitive information is not retained on Salesforce servers, protecting customer privacy and complying with data protection regulations. The zero data retention feature is a core architectural principle that prevents data leakage from AI interactions.

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Automatic model retraining

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Data masking for all PII

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Open-source model deployment

Question 18

A company uses Einstein Discovery to predict customer churn. They want to ensure the predictions are explainable to non-technical stakeholders. What is the best way to provide explanation?

Accepted Answer

Use the score factors feature to show top predictors. Option A is correct because Einstein Discovery's score factors feature explicitly lists the top predictors and their contributions to each prediction, making the model's reasoning transparent and accessible to non-technical stakeholders. This aligns with the need for explainable AI (XAI) without requiring deep data science knowledge.

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Share the full training dataset for transparency

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Allow stakeholders to retrain the model themselves

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Provide the raw model weights and coefficients

Question 19

A healthcare CRM administrator wants to use AI to recommend treatment plans based on patient data. Which combination of Salesforce Trusted AI principles is MOST critical to consider?

Accepted Answer

Accuracy and Empathy. Option C is correct because in healthcare AI, accuracy ensures that treatment recommendations are clinically valid and based on reliable patient data, while empathy ensures the system respects patient well-being and avoids harmful or insensitive suggestions. Without accuracy, the model could recommend incorrect treatments; without empathy, it might ignore patient context or ethical considerations. These two principles together directly address the dual need for clinical correctness and human-centered care in treatment planning.

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Safety and Honesty

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Honesty and Transparency

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Transparency and Safety

Question 20

A lead scoring model trained on historical sales data is found to assign lower scores to leads from certain postal codes. What is the MOST likely cause?

Accepted Answer

The training data contains biased outcomes from past human decisions. The model assigns lower scores to leads from certain postal codes because the training data reflects historical human biases, such as sales representatives prioritizing leads from affluent areas. Machine learning models learn patterns from the data they are trained on; if past sales decisions were biased against certain regions, the model will replicate those biases. This is a classic case of bias in training data leading to biased model outcomes, not an inherent flaw in the algorithm itself.

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The model's algorithm is inherently biased against certain regions

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The model overfits to the training data

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The model was not trained long enough