AI Associate Ethical Considerations of AI Practice Test 16 — 15 Questions

Question 1

A marketing team at a retail company uses Salesforce Einstein to generate personalized product recommendations. The model analyzes customer browsing history, purchase data, and demographic information including age and gender. A customer support representative receives a complaint from a customer who is concerned about how their data is being used. Which ethical principle is most directly relevant to addressing this customer's concern?

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

Accepted Answer

Transparency. The customer's concern centers on understanding how their personal data is used by the AI recommendation system. Transparency is the ethical principle that mandates clear disclosure of data practices, enabling customers to know what data is collected and how it influences AI decisions. By prioritizing transparency, the company can directly address the complaint and build trust, making it the most relevant principle here.

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Accountability

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Fairness

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Reliability

Question 2

A financial services company uses Einstein Prediction Builder to forecast loan default risk. The model includes zip code as a feature. An audit reveals that zip code strongly correlates with race due to historical housing segregation. Which ethical principle is most directly violated if the model is deployed as is?

Accepted Answer

Fairness. Zip code can act as a proxy for race, and using it in a loan default model can produce disparate impact against racial minorities. This violates the ethical principle of fairness, which demands that AI systems avoid unjust discrimination. Even if race is not explicitly used, the correlation makes the model unfairly biased, so it should not be deployed without mitigation.

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Privacy

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Accountability

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Transparency

Question 3

A Salesforce admin is configuring the Einstein Trust Layer for a generative AI feature that summarizes customer cases. Which two actions should the admin take to uphold ethical AI principles? (Choose two.)

Accepted Answer

Configure audit trail logging to record all prompts and responses for compliance review.. Enabling data masking and audit trail logging are key ethical practices for the Einstein Trust Layer. Data masking protects PII from being sent to external LLMs, while audit logging ensures accountability and transparency. Together, they uphold privacy and compliance, aligning with Salesforce's responsible AI principles.

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Disable all safety guardrails to allow the AI to generate more creative responses.

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Allow the AI to access all customer records to provide more accurate summaries.

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Store all generated summaries in a public repository for transparency.

Question 4

A Salesforce administrator is configuring Einstein Trust Layer for a new AI-powered customer service bot. The bot will summarize customer conversations and suggest responses. The administrator wants to ensure that personally identifiable information (PII) such as credit card numbers and social security numbers is not sent to the large language model (LLM). Which feature of Einstein Trust Layer should be enabled to achieve this?

Accepted Answer

Data masking. Data masking in Einstein Trust Layer identifies and redacts sensitive information such as credit card numbers and social security numbers before the data is sent to the LLM. This directly addresses the administrator's goal of preventing PII from reaching the external model, thereby upholding ethical AI practices around privacy and data minimization.

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Zero data retention

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Toxicity detection

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Audit trail

Question 5

A Salesforce administrator at a retail company is deploying an Einstein-powered product recommendation engine. The marketing team wants to use customer browsing history and purchase data to personalize recommendations. Which action best aligns with ethical AI principles regarding transparency and customer trust?

Accepted Answer

Provide customers with a clear, accessible explanation of how their data is used and offer an opt-out mechanism.. Providing a clear explanation and opt-out mechanism upholds transparency and user consent, which are fundamental to ethical AI. Salesforce's responsible AI principles stress the importance of informing users about data usage and giving them control. This builds trust and aligns with regulations like GDPR. The other options either ignore transparency, lack documentation, or fail to provide user choice.

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Deploy the recommendation engine immediately without documentation, as it is an internal tool and not customer-facing.

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Enable the recommendation engine to collect additional data from third-party sources without notifying customers to improve accuracy.

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Use only aggregate data to avoid privacy concerns, even if it reduces the personalization quality.

Question 6

A financial institution uses Salesforce Einstein to predict credit risk. The model's decisions are opaque, and customers are not informed about how their creditworthiness is determined. Which ethical principle is most directly compromised?

Accepted Answer

Transparency. The scenario highlights that the AI model's decisions are opaque and customers are not informed about how their creditworthiness is determined. This directly undermines transparency, which is the ethical principle requiring that AI systems be explainable and that individuals understand the basis of decisions affecting them. Transparency is essential for trust, regulatory compliance, and enabling customers to contest decisions. Therefore, transparency is the most directly compromised principle.

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Accountability

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Privacy

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Fairness

Question 7

A Salesforce admin is configuring Einstein Trust Layer for a generative AI feature in Service Cloud. The company's legal team wants to ensure that sensitive customer data, such as credit card numbers, is not sent to the large language model (LLM) provider. Which Salesforce feature should the admin enable to achieve this?

Accepted Answer

Data Masking. Einstein Trust Layer includes Data Masking, which automatically identifies and masks sensitive data such as credit card numbers before prompts are sent to the LLM. This ensures that external AI services never receive sensitive information, addressing privacy and ethical concerns. Other options do not provide this specific pre-processing masking capability.

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Field-Level Encryption

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Shield Platform Encryption

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Einstein Data Detection

Question 8

A Salesforce customer service team plans to deploy an Einstein Bots chatbot that handles billing inquiries. To uphold ethical AI principles, which practice should be implemented when the bot cannot resolve a customer's issue?

Accepted Answer

Escalate the conversation to a human agent and inform the customer.. When an AI chatbot cannot resolve an issue, escalating to a human agent and informing the customer is the ethical choice. It ensures the customer gets help, maintains transparency about the AI's limitations, and upholds human oversight. This approach respects the user and aligns with responsible AI practices that prioritize customer welfare over automation metrics.

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Keep the customer in a loop of automated responses until they give up.

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Ask the customer to call a phone number that is not staffed.

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Have the bot automatically close the case to improve efficiency metrics.

Question 9

A financial institution uses Salesforce Einstein to generate personalized investment advice. The AI model occasionally recommends high-risk products to elderly clients with conservative risk profiles. Which ethical principle is most directly violated?

Accepted Answer

Beneficence, because the AI is not acting in the best interest of the client.. Beneficence is the ethical principle of acting in the best interest of others. The AI's recommendations of high-risk products to conservative elderly clients directly harm their financial well-being, violating this principle. Ethical AI must prioritize user welfare and tailor advice to individual circumstances, ensuring that recommendations are suitable and beneficial.

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Transparency, because the AI does not explain why it recommends high-risk products.

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Privacy, because the AI accesses elderly clients' financial data without consent.

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Justice, because the AI treats elderly clients differently from younger clients.

Question 10

A financial services company uses Salesforce Einstein to predict which customers are likely to default on loans. The model was trained on historical loan data that includes sensitive attributes such as race and gender. An audit reveals that the model's predictions disproportionately reject applicants from certain demographic groups. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness. The model's biased predictions against certain demographic groups violate the ethical principle of fairness, which demands equitable treatment and non-discrimination. In AI, fairness ensures that models do not perpetuate historical biases or create unjust outcomes. This is especially critical in loan approvals, where biased decisions can have severe financial and social consequences.

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Privacy

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Transparency

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Accountability

Question 11

A developer at a financial services company is using Einstein Prediction Builder to create a model that predicts whether a customer will default on a loan. The training dataset includes a field 'ZipCode' that strongly correlates with race due to historical redlining. The developer wants to ensure the model is ethically sound. What is the most appropriate action?

Accepted Answer

Remove the 'ZipCode' field from the training dataset to avoid proxy discrimination.. Using ZipCode in a loan default model can introduce proxy discrimination because ZipCode often correlates with race due to historical housing policies. The most ethical action is to remove such proxy variables from the training data to prevent the model from learning and perpetuating bias. Post-hoc mitigation or using less granular data does not fully address the root cause.

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Include 'Race' as a feature and then apply fairness constraints to balance outcomes.

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Replace 'ZipCode' with a less granular geographic field like 'State' to reduce bias.

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Keep 'ZipCode' but add a bias mitigation algorithm after model training.

Question 12

A data scientist is building an AI model on Salesforce to predict customer churn. The model uses historical data that includes sensitive attributes such as age, gender, and postal code. During testing, the model shows high accuracy but disproportionately predicts churn for older customers. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness. The model's disproportionate impact on older customers is a clear case of bias, violating the ethical principle of fairness. Fairness demands that AI systems treat all groups equitably and avoid discrimination. While transparency, accountability, and privacy are important, they do not directly address the discriminatory outcome. Salesforce's responsible AI principles prioritize fairness and bias mitigation, making it the correct answer.

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Accountability

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Privacy

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Transparency

Question 13

A company uses Salesforce Einstein to analyze customer emails and extract sentiment. The AI model was trained on data that included personal health information without consent. Which ethical principle is most directly violated?

Accepted Answer

Privacy. The scenario describes training an AI model on personal health information without consent, which is a clear violation of privacy. Privacy is the ethical principle that safeguards individuals' control over their personal data, especially sensitive information like health records. Without consent, the company infringes on individuals' rights and may violate regulations like HIPAA or GDPR. Thus, privacy is the most directly violated principle.

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Accountability

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Transparency

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Fairness

Question 14

A Salesforce developer is integrating Einstein Language services to analyze customer feedback. Which TWO actions align with ethical AI principles when handling the data? (Choose two.)

Accepted Answer

Anonymize personally identifiable information before sending text to the AI service.. Anonymizing PII and obtaining informed consent are both essential for ethical AI. Anonymization protects privacy by removing identifiers, while consent ensures customers agree to the use of their data. Together, they uphold transparency, data minimization, and respect for user autonomy, aligning with responsible AI principles and privacy regulations.

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Share the feedback data with third-party advertisers to monetize the AI service.

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Use the feedback to train models without notifying customers, as long as it improves service.

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Store the raw customer feedback indefinitely for future model training.

Question 15

A Salesforce developer is deploying an AI model that predicts employee attrition. The model uses historical data that includes performance ratings, which are known to be biased against certain demographic groups. Which step should the developer take to address this ethical concern?

Accepted Answer

Audit the performance ratings for bias and adjust the model to use bias-mitigated features.. Auditing performance ratings for bias and applying mitigation techniques directly addresses the ethical concern of discrimination. This ensures the AI model promotes fairness and does not perpetuate historical inequalities. It is a necessary step to align with ethical AI principles and legal requirements.

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Use the model only for advisory purposes and let managers make final decisions.

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Deploy the model as is, since historical data reflects real-world outcomes.

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Remove the performance ratings entirely and rely on other predictors like tenure and salary.