AI Associate Ethical AI and Data Privacy Practice Test 2 — 15 Questions

Question 1

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 2

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 3

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 4

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 5

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 6

A company deploying Einstein Bots for customer service wants to ensure compliance with GDPR's right to explanation. Which TWO measures should they implement?

Accepted Answer

Implement an audit trail that logs which AI decisions were made and why. Option A is correct because GDPR's right to explanation requires organizations to provide meaningful information about the logic involved in automated decision-making. An audit trail that logs which AI decisions were made and why directly satisfies this by recording the decision path, input features, and model version used, enabling retrospective explanation. Option C is correct because score factors (e.g., feature importance weights in Einstein Bot's predictive models) allow the bot to show why a specific action was recommended, fulfilling the transparency requirement under Article 22 of GDPR.

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Store all customer chat transcripts permanently

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Use only rule-based bots to avoid AI complications

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Allow customers to opt out of all AI interactions

Question 7

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 8

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. Option B is correct because 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 9

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 10

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. Option B 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 11

Which Salesforce Trusted AI principle emphasizes that AI systems should be designed to benefit people and avoid causing harm?

Accepted Answer

Empathy. The Salesforce Trusted AI principle of Empathy emphasizes that AI systems should be designed with a human-centered approach, ensuring they benefit people and avoid causing harm. This principle guides the ethical development of AI by prioritizing user well-being and societal impact over purely technical or business objectives.

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Safety

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Accuracy

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Honesty

Question 12

A company uses Einstein to generate automated email responses. To comply with CCPA, which THREE practices should they adopt?

Accepted Answer

Provide an opt-out mechanism for customers who do not want AI-generated communications. Option C is correct because the CCPA grants consumers the right to opt out of the sale or use of their personal information for automated decision-making, including AI-generated communications. Einstein’s automated email responses rely on customer data, so providing a clear opt-out mechanism ensures compliance with CCPA’s requirement for consumer control over data-driven communications.

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Store all AI-generated emails indefinitely for auditing

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Obtain consent only once at account creation

Question 13

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. Option C is correct because 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 14

What is the role of the Data Processing Addendum (DPA) in the context of AI and data privacy on Salesforce?

Accepted Answer

It contractsually obligates Salesforce to protect customer data and outlines data processing terms for AI services. The Data Processing Addendum (DPA) is a legally binding contract between Salesforce and its customers that defines how Salesforce will process customer data, including data used by AI features like Einstein. It ensures Salesforce adheres to data protection laws (e.g., GDPR, CCPA) by specifying data handling, security measures, and sub-processor obligations, making it essential for compliance when using AI services on the platform.

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It lists all AI features available in Salesforce

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It provides a template for customers to build their own AI models

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It is a user guide for configuring AI privacy settings

Question 15

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