AI Associate Ethical Considerations of AI Practice Test 18 — 15 Questions

Question 1

A Salesforce administrator at a retail company is configuring Einstein Trust Layer for a new customer service chatbot that uses an external large language model (LLM). The administrator enables Data Masking and Zero Data Retention. During testing, the chatbot still includes a customer's full name and account number in its generated response. Which ethical principle is most directly being compromised by this behavior?

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

Accepted Answer

Privacy. The scenario describes a failure to protect personal data despite privacy controls, so the ethical principle most directly compromised is privacy. Data Masking and Zero Data Retention are meant to prevent sensitive information from being exposed, but the chatbot still outputs a full name and account number. This is a clear privacy violation, not primarily an issue of transparency, accountability, or inclusiveness.

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Transparency

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Accountability

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Inclusiveness

Question 2

A Salesforce developer is integrating an external large language model (LLM) with Salesforce to generate personalized marketing emails. The developer wants to ensure that the LLM does not generate harmful, biased, or inappropriate content. Which Einstein Trust Layer feature should be configured to filter out such content?

Accepted Answer

Toxicity detection. Toxicity detection is the Einstein Trust Layer feature that actively scans and filters AI-generated content for harmful, biased, or inappropriate language. By configuring it, the developer ensures that marketing emails generated by the LLM adhere to ethical standards and do not damage the brand or offend customers.

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Data masking

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Zero data retention

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Audit trail

Question 3

A financial services company uses Einstein Prediction Builder to predict loan default risk. The model is trained on historical loan data that includes zip code as a feature. An audit reveals that the model's predictions disproportionately flag applicants from low-income neighborhoods as high risk, even when controlling for credit score and income. Which ethical issue is most directly implicated?

Accepted Answer

Proxy discrimination. The model's use of zip code, which correlates with low-income neighborhoods, results in disproportionate high-risk predictions for applicants from those areas. This is proxy discrimination because zip code acts as a proxy for socioeconomic status or race, leading to unfair treatment. Even if the model does not explicitly use protected attributes, the outcome is discriminatory. The other options, while relevant to AI ethics, do not capture the specific harm of disparate impact caused by a correlated feature.

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Informed consent breach

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Lack of explainability

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Data minimization violation

Question 4

A Salesforce admin at a retail company is configuring Einstein Case Classification to automatically route customer support cases. The model will analyze case subject and description text. Which action best upholds the principle of transparency when deploying this AI feature?

Accepted Answer

Publish an internal wiki page explaining that Einstein Case Classification uses historical case data to suggest routing, and note that predictions may be overridden by support agents.. Transparency in AI means stakeholders understand how the system works, what data it uses, and its limitations. Informing support agents that Einstein Case Classification relies on historical case data and that predictions can be overridden gives them the context needed to use the tool responsibly. The other approaches either hide the AI's role or provide insufficient explanation, which undermines trust and ethical deployment.

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Deploy the model without notifying support agents, because the predictions are only suggestions and agents retain final decision-making authority.

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Enable Einstein Case Classification but restrict access to the model's prediction fields so only senior managers can view them.

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Configure the model to output only a confidence score without any explanation, and share the score with the support team.

Question 5

A Salesforce developer is building an AI-powered service that recommends product bundles to customers. The model uses historical purchase data and demographic attributes such as age and gender. During testing, the developer notices that the model recommends premium bundles almost exclusively to older male customers. The developer wants to address this ethical concern. Which action should the developer take first?

Accepted Answer

Remove age and gender from the model's training data and retrain the model.. The model is exhibiting bias by recommending premium bundles based on age and gender. The first ethical action is to remove those sensitive attributes from the training data and retrain the model. This directly tackles the source of discrimination. Other options either ignore the bias, worsen it, or apply superficial fixes that do not address the root cause, so removing sensitive attributes is the correct initial step.

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Adjust the recommendation threshold to favor premium bundles for all customers.

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Deploy the model as-is and monitor for customer complaints.

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Increase the model's complexity by adding more layers to improve accuracy.

Question 6

A marketing team at a retail company configures Einstein Content Selection to personalize product recommendations. During testing, they notice that the model disproportionately recommends luxury items to customers from affluent ZIP codes and budget items to those from lower-income ZIP codes, even though the training data did not include income directly. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness. The core ethical violation is fairness, because the model produces disparate outcomes for customers grouped by a socioeconomic proxy. Even if income was not a direct feature, ZIP code can encode socioeconomic status and lead to discriminatory personalization. Salesforce's responsible AI principles emphasize avoiding bias and ensuring equitable treatment, so the team must address the proxy variable and retrain or adjust the model to mitigate the disparate impact.

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Reliability and safety

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Accountability

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Transparency

Question 7

A Salesforce customer is deploying an Einstein AI model to predict customer churn. The company wants to ensure that its use of AI aligns with ethical principles of transparency and accountability. Which two actions should the company take? (Choose two.)

Accepted Answer

Establish a review board to evaluate the model's impact and address any ethical concerns.. Documenting the model in a model card and establishing a review board both promote transparency and accountability. A model card makes the model's purpose, data, and limitations clear, while a review board ensures ongoing oversight and responsibility. These actions align with ethical AI practices by making the system understandable and answerable to stakeholders. The other options either focus on privacy, reduce oversight, or are impractical for achieving transparency.

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Use the model to automatically make final decisions on customer retention offers without human review.

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Share the model's source code with all customers to demonstrate how it works.

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Enable Einstein Trust Layer data masking for all fields used by the model.

Question 8

A financial services company uses Salesforce Einstein Prediction Builder to score leads for a premium credit card offer. The model was trained on five years of historical lead data. An audit reveals that the model consistently assigns lower scores to leads from certain postal codes with historically lower average incomes. Which ethical principle is most directly compromised by continuing to use this model without intervention?

Accepted Answer

Fairness, because the model produces systematically lower scores for leads in lower-income postal codes, creating disparate impact.. Fairness in AI requires avoiding unjustified disparate impact on groups. Here, using postal code as a proxy for income causes the model to systematically disadvantage leads from lower-income areas, even though postal code is not a protected attribute. This is a classic proxy discrimination scenario. Mitigation could include removing or transforming the postal code feature, rebalancing training data, or applying bias detection tools before deployment.

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Reliability, because the model was trained on five years of data and may be outdated.

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Transparency, because the model's scoring formula is not disclosed to leads.

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Accountability, because no single person is named as responsible for the model's decisions.

Question 9

A Salesforce administrator is deploying an AI model that predicts customer churn. The model's decisions are not easily interpretable, and customers have asked how their data is used. Which ethical principle should the administrator prioritize to address customer concerns?

Accepted Answer

Transparency. Customers want to know how their data is used in the AI model, which is a matter of transparency. Transparency involves clear communication about data usage, model logic, and decision-making processes. While privacy, accountability, and inclusiveness are important ethical principles, they do not directly answer the customers' request for understanding. Therefore, prioritizing transparency is the correct action.

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Accountability

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Inclusiveness

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Privacy

Question 10

A Salesforce administrator at a bank deploys an Einstein Discovery model that predicts creditworthiness. The model's top predictor is a field called 'community_score' that was derived from historical lending data in majority-minority neighborhoods. The administrator wants to ensure the model complies with ethical AI guidelines. Which action best addresses the root cause of potential bias?

Accepted Answer

Investigate the historical data and feature engineering process that created community_score, then mitigate bias at the data and feature level before retraining.. The most effective action is to investigate and mitigate bias at the data and feature engineering stages, because the root cause is historical discrimination reflected in the community_score feature. Removing one field or adding a constraint does not address the underlying data problem. Salesforce's responsible AI guidance emphasizes understanding data provenance and correcting biased inputs. By auditing how community_score was created and applying data-level mitigation before retraining, the bank reduces the chance that bias persists through correlated features.

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Add a fairness constraint to the model that enforces equal approval rates across neighborhoods.

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Remove the community_score field from the model and retrain using only financial variables.

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Deploy the model but provide adverse action notices to applicants who are declined.

Question 11

A Salesforce administrator is setting up an Einstein AI feature that generates email responses to customers. The administrator wants to ensure that customers are aware they are interacting with an AI system. Which action should the administrator take?

Accepted Answer

Add a disclaimer in the email footer stating that the response was generated by AI.. The administrator should add a disclaimer in the email footer to inform customers that the response was generated by AI. This is a straightforward way to achieve transparency, ensuring customers are aware of the AI's involvement. The other options either mislead customers, focus on privacy, or gather feedback without disclosure. Transparency about AI usage is a key ethical requirement in customer-facing AI systems.

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Enable data masking for all customer personal information in the email.

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Configure the AI to use a human-like name and persona in the email signature.

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Set up a feedback survey after each AI-generated email to gauge customer satisfaction.

Question 12

A Salesforce developer is integrating an AI model that generates personalized marketing emails. To adhere to ethical AI principles, which two actions should the developer take? (Choose two.)

Accepted Answer

Ensure the model does not use sensitive personal data without explicit consent.. Ethical AI integration requires protecting sensitive data and respecting user choice. Ensuring sensitive data is not used without consent upholds privacy, while providing an opt-out respects autonomy. These two actions align with principles of privacy, transparency, and user control. The other options involve unrestricted data access, deception, or lack of oversight, which are unethical and should be avoided.

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Hide the fact that emails are AI-generated to maintain a human touch.

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Allow the model to access all customer data to maximize personalization.

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Use the model to automatically send emails without any human review.

Question 13

A Salesforce partner is building an AI-powered customer service chatbot using Einstein Bots and the Einstein Trust Layer. The client wants to ensure the deployment aligns with ethical AI practices. Which two actions should the partner take? (Choose two.)

Accepted Answer

Provide customers with a clear notice that they are interacting with an AI chatbot and offer an option to reach a human agent.. Ethical AI deployment with Einstein Bots and the Einstein Trust Layer requires protecting customer data and being transparent about AI use. Masking PII through the Trust Layer prevents sensitive information from reaching the LLM, while disclosing AI interaction and offering a human escalation path respects user autonomy. The other options either weaken accountability, introduce unreliable training data, or expose sensitive financial information.

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Disable all audit logging to reduce storage costs, since chatbot conversations are ephemeral.

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Allow the chatbot to collect and store credit card numbers in plain text to speed up payment processing.

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Train the chatbot exclusively on unverified public internet data to maximize the breadth of its knowledge.

Question 14

A Salesforce developer is implementing an AI-powered service agent that uses the Einstein Trust Layer. The company wants to ensure ethical AI practices are followed. Which two actions should the developer take to uphold the principle of accountability? (Choose two.)

Accepted Answer

Maintain an audit trail of all AI interactions, including prompts and responses, for review.. Accountability in AI means having clear responsibility and oversight for the system's actions. Maintaining an audit trail ensures that AI interactions can be reviewed and traced, supporting transparency and responsibility. Designating a responsible AI officer or team establishes human ownership and decision-making authority. Together, these practices ensure that the AI system is monitored and that someone is answerable for its outcomes. Other options address privacy, safety, and user feedback, which are important but do not directly fulfill accountability.

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Implement a feedback mechanism for users to report problematic AI responses.

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Enable toxicity detection to filter out harmful language in AI responses.

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Use data masking to redact personally identifiable information before sending prompts to the LLM.

Question 15

A Salesforce developer is building an Einstein Bot that handles customer service inquiries for a utility company. The bot must decide whether to escalate a customer's complaint to a human agent. The developer wants to ensure the bot's decision-making aligns with ethical AI principles. Which practice should be implemented?

Accepted Answer

Provide clear disclosure that the customer is interacting with an AI bot and offer an easy path to reach a human agent.. The ethical practice is to disclose the AI interaction and provide an easy path to a human agent. This respects customer autonomy, ensures transparency, and maintains human oversight for consequential decisions. Salesforce's responsible AI principles emphasize informing users when they are interacting with AI and preserving human control. Keyword escalation and unfiltered training do not systematically address fairness or accountability, and letting the bot make final decisions removes necessary human judgment.

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Configure the bot to always escalate complaints containing keywords like 'lawsuit' or 'discrimination' to a human agent.

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Allow the bot to make final decisions on all escalations to reduce call center costs and improve efficiency.

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Train the bot on historical chat logs without filtering to maximize its ability to handle all complaint types.