AI Associate Ethical AI and Data Privacy Practice Test 3 — 15 Questions

Question 1

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. 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 2

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. 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 3

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

Question 4

Which Salesforce feature allows administrators to mask personally identifiable information (PII) in prompts sent to large language models?

Accepted Answer

Einstein Trust Layer. The Einstein Trust Layer is a security architecture within Salesforce that intercepts prompts sent to large language models (LLMs) and automatically masks personally identifiable information (PII) before the data reaches the external AI service. After the LLM returns a response, the Trust Layer reinserts the masked data, ensuring that sensitive information never leaves the Salesforce ecosystem in plain text. This feature is specifically designed to address data privacy and compliance requirements when using generative AI features like Einstein GPT.

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Sharing Rules

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Permission Sets

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Data Export Service

Question 5

A marketing manager wants to use AI to generate personalized email content for customers. According to Salesforce's Trusted AI principles, what should the manager ensure before sending?

Accepted Answer

A human reviews the AI-generated content before sending. Salesforce's Trusted AI principles emphasize human oversight to ensure AI-generated content is accurate, appropriate, and aligned with brand values. The manager must have a human review the content before sending to mitigate risks like bias, inaccuracy, or inappropriate messaging, which aligns with the principle of accountability in AI deployment.

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The emails are sent automatically to maximize efficiency

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The emails are sent only to customers who opted in to AI interactions

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The AI model is retrained daily on new customer data

Question 6

What is the purpose of grounding in the Einstein Trust Layer?

Accepted Answer

To connect the AI to relevant CRM records so responses are accurate and context-aware. Grounding in the Einstein Trust Layer connects the AI model to relevant CRM records (e.g., Accounts, Opportunities, Cases) so that generated responses are based on accurate, up-to-date customer data rather than the model's general training data. This ensures context-aware and factual outputs while maintaining data privacy by not exposing raw records to the model.

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To limit the AI's responses to pre-approved templates

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To encrypt all data sent to the AI model

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To ensure the AI only uses data from Salesforce, not third-party sources

Question 7

A company is concerned about the data minimization principle when using AI to predict customer lifetime value. Which approach aligns with this principle?

Accepted Answer

Use only fields directly relevant to purchase history and engagement, excluding PII like social security numbers. Aligns with the data minimization principle by restricting data collection to only fields directly relevant to the prediction task (purchase history and engagement) and explicitly excluding personally identifiable information (PII) like social security numbers. This reduces privacy risk and complies with regulations such as GDPR and CCPA, which require that data collected be adequate, relevant, and limited to what is necessary for the processing purpose.

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Include all available fields to maximize model accuracy

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Obtain explicit consent for every field used

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Anonymize all data before training, including purchase amounts

Question 8

A data scientist is building a model to recommend products. They notice the model rarely recommends certain categories to users from a specific demographic. What should the scientist do first to address potential bias?

Accepted Answer

Audit the training data for representation and bias. The first step in addressing potential bias is to audit the training data for representation and bias. This involves examining whether certain categories or demographics are underrepresented or overrepresented in the dataset, which can lead to skewed model recommendations. Without understanding the data composition, other interventions may fail to address the root cause of bias.

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Increase the model's complexity to capture more patterns

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Add more features about the demographic to improve accuracy

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Remove the demographic feature from the model

Question 9

When a Salesforce admin enables 'Score Factors' for an AI prediction, what does this provide to end users?

Accepted Answer

A list of the most influential fields and their contribution to the prediction. When 'Score Factors' is enabled for an AI prediction in Salesforce, end users see a breakdown of the most influential fields that contributed to the prediction, along with their relative contribution (e.g., positive or negative impact). This provides transparency into why a specific prediction was made, helping users trust and act on the AI's output without exposing the underlying model logic.

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The exact formula used by the model

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A confidence interval for the prediction

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A histogram of all prediction values

Question 10

A sales operations manager wants to use Einstein Lead Scoring to prioritize leads. They have historical data showing that leads from a certain postal code have a low conversion rate. However, they suspect the low conversion is due to a past marketing campaign that was poorly targeted, not the demographics of that area. What is the BEST way to ensure the AI model does not unfairly penalize leads from that postal code?

Accepted Answer

Audit the model for disparate impact on that postal code and retrain with updated labels that reflect the true conversion potential. The best approach is to audit the model for disparate impact on the postal code and retrain with updated labels that reflect true conversion potential. This addresses the root cause — biased historical data from a poorly targeted campaign — rather than masking symptoms. Retraining with corrected labels ensures the model learns genuine patterns instead of perpetuating past marketing mistakes.

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Manually increase the lead scores for all leads from that postal code

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Remove the postal code field from the model training data entirely

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Use the model as-is because Salesforce AI is certified to be fair

Question 11

A Salesforce admin wants to ensure that customer data used by Einstein features is not retained by Salesforce to train foundation models. Which component of the Einstein Trust Layer enforces this commitment?

Accepted Answer

Zero Data Retention. Zero Data Retention is the Einstein Trust Layer component that contractually and technically ensures customer data sent to the LLM is not stored or used to train Salesforce's foundation models. It is a core commitment of the Trust Layer and directly addresses the admin's requirement. Other components like audit trail and PII masking serve different purposes.

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Audit trail

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Toxicity detection

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PII masking

Question 12

A company deploys an AI-powered email composer for sales reps. The legal team requires that every AI-generated email be reviewed by a human before sending to a customer. Which approach aligns with Salesforce's Trusted AI Principle of Empathy and Human Oversight?

Accepted Answer

Require the sales rep to click 'Approve' before the email is sent. It directly implements human oversight by requiring the sales rep to click 'Approve' before the email is sent, aligning with Salesforce's Trusted AI Principle of Empathy and Human Oversight. This principle mandates that AI systems should include mechanisms for human review and control, especially in high-stakes communications, ensuring that AI-generated content is vetted for accuracy, tone, and compliance before reaching customers. The approval step creates a clear human-in-the-loop checkpoint, preventing automated sending without human judgment.

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Allow the AI to send emails automatically but log all sent emails for audit

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Use a rule to block specific words in AI-generated emails

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Send a daily report of all AI-generated emails to the legal team after they are sent

Question 13

A data scientist is building a churn prediction model using Einstein Discovery. They want to ensure the model does not rely on sensitive attributes like race or gender, even if those are correlated with other features. Which technique is MOST aligned with Salesforce's data minimisation principle?

Accepted Answer

Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them. Data minimisation means using only the data necessary for the task. Excluding sensitive attributes from the feature set is the most direct way to prevent them from being used, even if they are correlated with other features. Correlation does not imply causation, and if those features are not essential, they should be removed.

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Include all features and rely on the AI to ignore biased ones

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Apply a fairness constraint after training to adjust predictions

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Use differential privacy to add noise to the training data

Question 14

A financial services company uses Einstein Bots to answer customer inquiries. A customer asks the bot to explain why their loan application was rejected. The bot provides a response based on AI predictions. Which Salesforce Trusted AI Principle is MOST directly addressed by the bot's ability to explain the decision?

Accepted Answer

Transparency. The Salesforce Trusted AI Principle of Transparency requires that AI systems be able to explain their decisions and actions in a way that is understandable to users. In this scenario, the Einstein Bot is providing an explanation for why the loan application was rejected, which directly addresses the need for transparency in AI-driven decisions. This principle ensures that customers and stakeholders can understand the reasoning behind AI outcomes, building trust and accountability.

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Honesty

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Accuracy

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Safety

Question 15

A company wants to use Einstein Sentiment Analysis to classify customer feedback. What is the FIRST step they should take to ensure ethical use of customer data?

Accepted Answer

Obtain proper consent from customers for using their data in AI. Before using customer data in any AI system, including Einstein Sentiment Analysis, the organization must obtain proper consent from customers for that specific use — this is the foundational ethical and often legal requirement (e.g., under GDPR, CCPA). Consent establishes the lawful basis for processing and ensures transparency about how the data will be used. Only after consent is secured should technical measures like anonymization or audit trails be considered.

Answer

Set up an audit trail for all AI actions

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Choose a pre-trained model to avoid using customer data

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Anonymize all customer feedback before analysis