AI Associate Ethical AI and Data Privacy Practice Test 5 — 15 Questions

Question 1

What is the primary purpose of the Einstein Trust Layer's zero data retention setting?

Accepted Answer

To ensure that customer data is not used to train or improve Salesforce's base AI models.. The Einstein Trust Layer's zero data retention setting ensures that customer data processed by Salesforce's AI models is not stored or used to train or improve the base AI models. This addresses privacy and compliance concerns by preventing data leakage into model training pipelines.

Answer

To comply with Salesforce's internal data management policies only.

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To improve model accuracy by preventing old data from influencing predictions.

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To reduce storage costs for the customer.

Question 2

A company is using Einstein Article Recommendations in Service Cloud. They notice that articles about a specific product are never recommended, even when relevant. After reviewing, they find that the training data did not include any cases where that product was mentioned. Which Salesforce Trusted AI principle is most directly violated?

Accepted Answer

Accuracy. The Accuracy principle of Salesforce Trusted AI requires that AI systems be designed to produce accurate, reliable, and unbiased results. If the training data lacks cases mentioning a specific product, the model cannot learn to recommend articles about it, leading to inaccurate recommendations. This directly violates the Accuracy principle.

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Empathy

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Honesty

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Safety

Question 3

A Salesforce administrator is setting up Einstein Next Best Action for a marketing campaign. They want to ensure that customer consent preferences are respected. Which action should they take?

Accepted Answer

Configure the recommendation strategy to check the 'HasOptedOutOfEmail' field and exclude customers who have opted out.. Einstein Next Best Action recommendations must respect customer consent and communication preferences. Configuring the recommendation strategy to check the 'HasOptedOutOfEmail' field and exclude opted-out customers ensures compliance with consent requirements. This directly enforces the marketing preference at the point of recommendation generation.

Answer

Include all customers and let the AI decide who to target, as consent is not relevant for recommendations.

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Anonymize customer data before running the AI model so consent is not needed.

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Use a data extension to store consents and manually update it weekly.

Question 4

A company is deploying an AI model to automatically approve or reject small loan applications. To comply with the right to explanation under GDPR, what capability must the system provide?

Accepted Answer

The ability for the applicant to chat with a human agent and receive the specific reasons for the decision.. GDPR's right to explanation (Articles 13-15 and 22) requires that individuals subject to automated decision-making receive meaningful information about the logic involved and the specific reasons for the decision. Providing a human agent who can explain the specific reasons for the loan decision satisfies this requirement. This is the only option that delivers individualized, understandable reasoning.

Answer

A downloadable PDF report of the model's accuracy metrics.

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A live video feed of the data center where the model runs.

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A promise that the model was certified by an external auditor.

Question 5

A sales manager is reviewing the AI predictions from Einstein Opportunity Scoring. Which THREE actions should the manager take to ensure ethical use of AI according to Salesforce Trusted AI principles?

Accepted Answer

Provide sales reps with explanations of the key factors influencing each score.. Option A is correct because Salesforce's Trusted AI principles emphasize transparency and explainability, so giving reps the key factors behind each Einstein Opportunity Scoring prediction lets them understand and appropriately act on the AI output. Option B is correct because responsible AI requires ongoing monitoring and validation of model accuracy against real outcomes, ensuring the predictions remain reliable and unbiased over time. Option E is correct because human oversight is a core Trusted AI tenet; encouraging judgment and escalating borderline cases keeps a human in the loop for consequential decisions. Option C is wrong because hiding the model's logic contradicts transparency and explainability rather than preventing harm. Option D is wrong because overriding scores without review undermines accountability and human oversight, replacing informed judgment with unchecked intuition.

Answer

Keep the model's logic secret to prevent gaming the system.

Answer

Override the scores without review if they conflict with intuition.

Question 6

A retailer is using Einstein Product Recommendations on their ecommerce site. They want to avoid biased recommendations that might disadvantage certain customer groups. Which THREE steps should they take?

Accepted Answer

Provide customers with a reason why a particular product was recommended.. To avoid biased recommendations, the retailer should audit outputs for disparate impact (B), include diverse training data to represent all groups (E), and provide customers with reasons for recommendations to ensure transparency (A). Removing all demographic data from model features (C) is not recommended because it can prevent detection of bias and may not eliminate it; instead, the goal is to ensure fair representation and accountability.

Answer

Remove all customer demographic data from the model features.

Answer

Use only historical purchase data to train the model.

Question 7

A sales operations team notices that an Einstein Lead Scoring model assigns lower scores to leads from a particular geographic region, even though those leads have historically converted at a higher rate. What is the most likely cause of this discrepancy?

Accepted Answer

The model has been trained on biased historical data that underrepresents or undervalues leads from that region. Einstein Lead Scoring learns patterns from historical lead and conversion data, so if leads from a particular region converted at a higher rate but the model scores them lower, the training data likely contains a bias — for example, underrepresentation of that region or historical labeling that undervalued those leads. The model reproduces the statistical patterns it was trained on, including any systemic bias. This is a classic example of biased training data producing unfair or inaccurate predictions.

Answer

The model is using outdated lead source fields

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The model's threshold for lead conversion is set too high

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The model is overfitted to noise in the data

Question 8

A healthcare organization uses Einstein Prediction Builder to predict patient no-show rates. They want to ensure that protected health information (PHI) like patient names and social security numbers are not used in the model. Which Salesforce Trusted AI principle or feature directly addresses this requirement?

Accepted Answer

Data minimisation. Data minimisation is a key principle: only use relevant features for the model and avoid including sensitive PII unnecessarily. The Einstein Trust Layer's PII masking also helps, but the principle that directly addresses using only relevant fields is data minimisation.

Answer

Zero data retention

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Explainability

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Human oversight

Question 9

A marketing manager wants to understand why a specific lead received a high score from an Einstein model. Which Salesforce feature provides the most detailed explanation?

Accepted Answer

Einstein Discovery. Einstein Discovery is Salesforce's augmented analytics and machine-learning feature that builds predictive models and, critically, surfaces the top predictors driving each individual prediction through its explanation/insight cards. When a lead receives a high score, Einstein Discovery shows which fields (e.g., industry, engagement, company size) contributed most and in which direction. This per-record explainability is exactly what the marketing manager needs.

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Einstein Bots

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Einstein Activity Capture

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Einstein Automate

Question 10

A data scientist is building a custom AI model using Salesforce Data Cloud to predict customer churn. They want to ensure that the model does not inadvertently use gender as a feature to avoid biased predictions. Which step is MOST appropriate?

Accepted Answer

Exclude gender from the feature set used for model training. The most direct and appropriate step to prevent gender-based bias is to exclude gender from the feature set used for model training, ensuring the model cannot learn gender-correlated patterns. This is a pre-processing fairness technique that removes the protected attribute before training. While not a complete solution (proxy variables can still encode gender), it is the standard first-line mitigation and the most appropriate answer among the options.

Answer

Use gender as a feature but ignore the model predictions for certain groups

Answer

Allow gender in training but use a post-processing technique to adjust scores

Answer

Include gender as a feature and then apply a fairness constraint during training

Question 11

A sales rep receives an AI-generated recommendation to upsell a product to a customer. The rep wants to verify the reasoning behind the recommendation before acting. What Salesforce feature can the rep use to see the key factors that influenced the recommendation?

Accepted Answer

Einstein Discovery score factors. Einstein Discovery score factors provide insights into the key factors that influenced a prediction or recommendation. When a sales rep receives an AI-generated recommendation, they can view the score factors to understand why the recommendation was made, such as which customer attributes or behaviors contributed most to the score. Einstein Next Best Action delivers recommendations but does not inherently show the reasoning; Activity Capture logs activities; Bots are for automated conversations.

Answer

Einstein Next Best Action

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Einstein Activity Capture

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Einstein Bots

Question 12

Which Salesforce Trusted AI principle ensures that users are informed when they are interacting with an AI-generated output or recommendation?

Accepted Answer

Transparency. The Salesforce Trusted AI principle of Transparency ensures that users are informed when they are interacting with AI-generated outputs or recommendations. This principle requires clear disclosure about AI involvement, enabling users to understand the context and limitations of the AI system. It aligns with the ethical goal of building trust by making AI operations visible and understandable.

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Safety

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Accuracy

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Honesty

Question 13

A company is using Einstein Service Replies to generate suggested responses for service agents. They want to ensure that agents always review and approve the suggested reply before it is sent to the customer. Which configuration best supports this requirement?

Accepted Answer

Configure Einstein Service Replies to require agent approval before sending. Einstein Service Replies can be configured to generate suggested responses for agents. To ensure agents review and approve before sending, you must enable the setting that requires agent approval. This is typically a configuration option in Service Cloud that prevents auto-send and forces the agent to edit or approve the reply. Auto-send would bypass review, toxicity detection is unrelated to approval, and using Einstein Bots is a different feature for automated conversations, not agent-assisted replies.

Answer

Use Einstein Bots instead of Service Replies

Answer

Turn on toxicity detection for all replies

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Enable auto-send for Einstein Service Replies

Question 14

A data governance officer wants to ensure that customer data used in Einstein Prediction Builder is not retained by Salesforce after the prediction is made. Which Einstein Trust Layer capability guarantees this?

Accepted Answer

Zero data retention. Zero data retention in the Einstein Trust Layer ensures that customer data used for predictions is not stored or retained by Salesforce after the prediction is processed. This capability is critical for compliance with data privacy regulations. It guarantees that data is discarded immediately post-prediction, preventing any persistent storage.

Answer

Grounding

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Audit trail

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PII masking

Question 15

A company is developing an AI system that makes loan approval decisions. Under GDPR, customers have the right to request an explanation of how the decision was made. Which Salesforce feature provides this explanation?

Accepted Answer

Einstein Discovery prediction explanations. Einstein Discovery prediction explanations is correct because it provides human-readable, factor-by-factor explanations of how a model arrived at a specific prediction, including the relative influence of each contributing variable. This directly satisfies GDPR Article 22's requirement for meaningful information about the logic involved in automated decisions. Salesforce exposes these explanations through the Einstein Discovery prediction explanation feature, which can be surfaced in CRM records and APIs.

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Einstein Bots

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Einstein Trust Layer audit trail

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Einstein Next Best Action