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

Question 1

A company is subject to GDPR and wants to use customer purchase history to predict future buying behavior. What is the primary requirement they must fulfill under GDPR when using customer data for AI predictions?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Obtain explicit consent from customers to use their data for AI predictions. Under GDPR, processing personal data for AI predictions requires a lawful basis. Explicit consent is one such basis, and it is often required when data is used for new purposes like AI predictions. Therefore, obtaining explicit consent is a primary requirement.

Answer

Ensure the AI model is hosted in the same country as the customer

Answer

Provide a discount to customers who opt in to data usage

Answer

Anonymize all customer data before training the model

Question 2

A company wants to use AI to automatically qualify leads without human intervention. However, they are concerned about potential bias in the model. Which Salesforce approach can help them detect and mitigate bias in their lead scoring model?

Accepted Answer

Einstein Trust Layer bias detection. Einstein Trust Layer includes bias detection capabilities that help identify and mitigate bias in AI models, including lead scoring. It provides tools to audit model outputs for fairness and can flag potential bias before predictions are used. This directly addresses the company's concern about bias in automated lead qualification.

Answer

Einstein Activity Capture

Answer

Einstein Automate

Answer

Einstein Bots

Question 3

A sales operations manager wants to ensure that the AI-driven lead scoring model in Salesforce does not discriminate against certain demographic groups. Which Salesforce tool or feature should they use to regularly check for bias in the model's predictions?

Accepted Answer

Use Einstein Trust Layer audit trail and model explainability features. To regularly check for bias in an AI-driven lead scoring model, the sales operations manager should use the Einstein Trust Layer's audit trail and model explainability features. These tools provide transparency into how the model makes predictions and allow auditing of outcomes across different demographic groups, helping identify and mitigate bias.

Answer

Implement Data Processing Addendum (DPA) for all customer data

Answer

Enable zero data retention policy to prevent data leakage

Answer

Use Data Cloud to merge all demographic data into a single profile

Question 4

A healthcare organization uses Einstein Next Best Action to recommend treatment plans to practitioners. A patient disputes a recommendation, claiming it was based on inaccurate historical data. Under GDPR, the patient has the right to obtain an explanation of the automated decision. Which Salesforce feature directly supports this right to explanation?

Accepted Answer

Einstein Trust Layer audit trail and model explainability. The Einstein Trust Layer provides audit trails and model explainability features that document how AI-driven recommendations are generated, directly supporting GDPR's right to explanation for automated decisions. This allows the healthcare organization to provide patients with an explanation of the basis for a treatment recommendation.

Answer

Einstein Activity Capture to gather customer interactions

Answer

Data Processing Addendum (DPA) signed with Salesforce

Answer

Data Cloud permission sets to control data access

Question 5

A financial services company uses Salesforce Einstein to generate personalized investment recommendations. The compliance team is concerned about the AI model inadvertently learning and reproducing sensitive client information from training data. Which Einstein Trust Layer capability directly addresses this risk by preventing the model from memorizing and regurgitating training data?

Accepted Answer

Differential Privacy. Differential Privacy is a training-time technique that injects noise to prevent models from memorizing individual data points, directly mitigating the risk of reproducing sensitive client information. Data Masking and Zero Data Retention operate at inference time and do not affect what the model learned during training. Toxicity Detection addresses content safety, not privacy leakage. Therefore, Differential Privacy is the correct capability.

Answer

Zero Data Retention

Answer

Data Masking

Answer

Toxicity Detection

Question 6

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.

Answer

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

Answer

To automatically generate AI models without any human oversight

Answer

To train large language models on customer data for better predictions

Question 7

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. 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.

Answer

Enabling zero data retention in the Trust Layer

Answer

Data anonymization via the Einstein Trust Layer

Answer

Regularly auditing the model for bias

Question 8

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.

Answer

Allow the AI to send emails automatically if confidence is high

Answer

Disable the AI composer entirely to avoid GDPR risk

Answer

Anonymize customer data before drafting

Question 9

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. 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.

Answer

Einstein Activity Capture

Answer

Einstein Copilot prompt template

Answer

Einstein Trust Layer audit trail

Question 10

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.

Answer

To increase the speed of AI responses

Answer

To monitor user interactions for compliance with data protection laws

Answer

To identify and mask PII in user prompts

Question 11

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. Under the Salesforce Data Processing Addendum (DPA), Salesforce commits that customer data is not used to train or improve Salesforce's base AI models. This is a contractual data-protection commitment that applies to AI services such as Einstein, ensuring customer data remains the customer's and is not repurposed to enhance shared foundation models.

Answer

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

Answer

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

Answer

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

Question 12

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

Accepted Answer

Einstein Trust Layer audit trail. 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.

Answer

Einstein Discovery

Answer

Einstein Copilot

Answer

Einstein Prediction Builder

Question 13

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. 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

Answer

Remove demographic data from the model entirely and continue using it

Answer

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

Question 14

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. 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.

Answer

PII masking

Answer

Grounding

Answer

Toxicity detection

Question 15

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 (Articles 13-15 and Recital 71) requires that data subjects be given meaningful information about the logic involved in automated decision-making, and listing the most influential factors per recommendation directly satisfies this transparency requirement. Option E is correct because GDPR Article 22 grants individuals the right not to be subject to solely automated decisions with significant effects and provides the right to obtain human intervention, so offering a human review process when a customer requests an explanation operationalizes that safeguard. Option A is not required by the right to explanation; GDPR retention is governed by the storage limitation principle (Article 5(1)(e)), which favors keeping data no longer than necessary, not a fixed 10-year period. Option C addresses consent/opt-out rights under Articles 21-22 but is not the specific mechanism for delivering an explanation. Option D concerns data minimization and anonymization, which reduce privacy risk but do not by themselves fulfill the right to explanation for automated decisions.

Answer

Store all recommendation logs for at least 10 years

Answer

Allow customers to opt out of all AI recommendations

Answer

Use only anonymized data for recommendations