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

Question 1

A financial services firm deploys Einstein Prediction Builder to predict loan default risk. The model uses sensitive attributes like zip code and age. During testing, the model shows a disparate impact on minority neighborhoods. The compliance team requires explanation of individual predictions for regulatory audits. The data science team wants to use a complex deep learning model that is not interpretable. Which approach best balances performance and ethical responsibility?

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

Accepted Answer

Use a simpler, interpretable model (e.g., logistic regression) that may have slightly lower accuracy but ensures transparency and reduces bias.. In a regulated financial-services scenario, a simpler interpretable model such as logistic regression provides inherently transparent, auditable individual predictions and makes it easier to detect, document, and mitigate disparate impact on protected groups like minority neighborhoods, even at some cost in accuracy. The compliance requirement for explaining individual predictions is best met by a model whose decision logic is directly inspectable rather than one requiring post-hoc approximations. Options A and B retain the uninterpretable deep learning model, so individual explanations remain approximate and bias risk persists, and limiting exposure does not satisfy the audit requirement. Option C is unethical and likely illegal, since adjusting thresholds per group to conceal disparate impact is discriminatory manipulation rather than a valid fairness remedy.

Answer

Use the complex model but provide post-hoc explanations like SHAP values to satisfy compliance.

Answer

Use the complex model but only for a subset of customers to limit exposure.

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Use the complex model and hide the disparate impact by adjusting thresholds per group.

Question 2

A company has deployed an AI-powered chatbot to handle customer service inquiries. The chatbot is designed to answer frequently asked questions and escalate complex issues to human agents. Which action best aligns with ethical AI principles regarding transparency?

Accepted Answer

Clearly disclose that the chatbot is an AI and provide an option for customers to switch to a human agent.. Option D is correct because ethical AI transparency requires that users know they are interacting with an AI system, and providing an easy escalation path to a human agent respects user autonomy and informed consent. Disclosing the chatbot's AI nature and offering a human alternative directly satisfies transparency and accountability principles for customer-facing AI. Option A is wrong because fully autonomous handling without disclosure or escalation removes human oversight and hides the AI's role. Option B is wrong because training on unaudited historical data can perpetuate bias and violates fairness and accountability, not transparency. Option C is wrong because impersonating a human agent is deceptive and directly contradicts transparency requirements.

Answer

Configure the chatbot to answer all queries without human intervention to maximize efficiency.

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Use historical customer interaction data without auditing for bias to train the chatbot's responses.

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Program the chatbot to identify itself as a human agent to build trust with customers.

Question 3

An organization uses an AI-powered resume screening tool to shortlist candidates for a software engineering role. The tool was trained on historical hiring data from the past five years, during which the company predominantly hired male candidates. After deployment, the tool consistently ranks female candidates lower, even when they have equivalent qualifications. The AI team reports that the overall model accuracy is 92%, and they argue that performance is strong. However, the diversity and inclusion team raises ethical concerns about gender bias. The Salesforce AI Associate is asked to evaluate the situation. What should the associate recommend?

Accepted Answer

Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.. The 92% overall accuracy metric masks a disparate impact on a protected group, which is a classic fairness failure in AI systems trained on biased historical data. Retraining with a balanced, representative dataset directly addresses the root cause (sampling bias in the training data), and ongoing fairness monitoring ensures the model does not drift back into biased behavior after deployment. This aligns with the Salesforce AI Associate principle of building trustworthy, ethical AI that is fair, inclusive, and continuously evaluated.

Answer

Continue using the model because 92% accuracy is acceptable and the bias is not significant.

Answer

Replace the current AI tool with a different vendor's tool without further analysis.

Answer

Manually adjust the scoring algorithm to give preference to female candidates to balance the outcome.

Question 4

A Salesforce admin is enabling Einstein Generative AI in their org. To comply with ethical AI guidelines, they enable data masking in the Einstein Trust Layer. Which risk does this primarily mitigate?

Accepted Answer

Sensitive customer data being exposed to the large language model.. Data masking in the Einstein Trust Layer is designed to protect sensitive information by replacing it with placeholders before it reaches the LLM. This prevents exposure of PII and other confidential data, aligning with ethical AI principles of privacy and security. The other options describe risks that masking does not directly mitigate, such as bias, internal access control, or data quality.

Answer

Biased outputs generated by the large language model.

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Unauthorized access to Salesforce records by internal users.

Answer

Inaccurate predictions from the AI model due to poor data quality.

Question 5

A retail company uses Salesforce Einstein to generate personalized product recommendations. The data science team discovers that the model recommends high-interest credit products primarily to customers from lower-income zip codes, even when those customers have similar credit profiles to others. The company wants to address this ethical concern. Which action best aligns with Salesforce's responsible AI principles?

Accepted Answer

Conduct a bias audit, document the findings, and apply mitigation techniques such as reweighting or adversarial debiasing before redeploying.. The correct action is to conduct a bias audit, document findings, and apply mitigation techniques. This aligns with Salesforce's responsible AI principles of fairness, transparency, and accountability. By systematically identifying and addressing bias, the company can reduce disparate impact and ensure ethical deployment. Simply removing a feature or adding a disclaimer does not adequately resolve the underlying issue.

Answer

Deploy the model as-is but add a disclaimer that recommendations may be biased.

Answer

Remove the zip code field from the training dataset and retrain the model.

Answer

Increase the model's accuracy by adding more training data from the same zip codes.

Question 6

A Salesforce customer service team uses Einstein Bots to handle customer inquiries. To ensure ethical AI, they want to provide transparency about the bot's capabilities. Which action should they take?

Accepted Answer

Clearly inform customers that they are interacting with an AI bot and provide an option to escalate to a human agent.. Ethical AI requires transparency so users understand they are interacting with an AI system. Informing customers and providing a human escalation option respects their autonomy and builds trust. The other options either deceive users, fail to disclose AI involvement, or violate privacy, none of which align with ethical AI principles.

Answer

Store all customer conversations for auditing purposes without notifying customers.

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Configure the bot to mimic human responses so customers do not realize they are talking to a machine.

Answer

Limit the bot's responses to predefined FAQs to avoid any unexpected answers.

Question 7

A financial institution uses Salesforce Einstein to generate personalized investment advice. The AI model suggests higher-risk investments to younger clients and lower-risk investments to older clients, based on age. A compliance officer raises concerns about ethical and regulatory implications. Which action should the company take to align with Salesforce's responsible AI principles?

Accepted Answer

Conduct a fairness assessment, document the use of age, and implement safeguards such as human review for high-risk recommendations.. The correct action is to conduct a fairness assessment, document the use of age, and implement safeguards like human review. This ensures the model is used ethically and transparently while still leveraging its benefits. It aligns with Salesforce's principles of fairness, accountability, and transparency. Simply removing age or disabling the model does not adequately address the ethical and regulatory concerns.

Answer

Disable the AI model and revert to manual investment advice to avoid any ethical risk.

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Continue using age as a factor because it is a legitimate business criterion for investment risk.

Answer

Remove age from the model and retrain it using only financial goals and risk tolerance assessments.

Question 8

A Salesforce administrator is configuring Einstein Generative AI for a customer service org. To adhere to ethical AI principles, which two actions should they take? (Choose two.)

Accepted Answer

Implement data masking for sensitive fields before sending data to the LLM.. Ethical AI requires accountability and privacy protection. Audit logging ensures transparency and traceability, while data masking prevents sensitive data exposure. The other options involve unrestricted data access, disabling human oversight, and using biased data, all of which violate ethical principles and increase risk.

Answer

Train the AI on all available data, including outdated and biased datasets, to maximize knowledge.

Answer

Allow the AI to access all customer data without restrictions to improve personalization.

Answer

Disable all human oversight to ensure faster AI responses.

Question 9

A Salesforce admin is implementing Einstein Prediction Builder for a retail company's churn prediction. The admin selects 'Customer Gender' and 'Ethnicity' as predictor fields to improve model accuracy. Which action should the admin take before deploying the model?

Accepted Answer

Remove the sensitive fields from the model and document the exclusion in the model's metadata.. Sensitive attributes like gender and ethnicity should not be used as predictors in AI models because they can lead to biased and discriminatory decisions. Removing them ensures fairness and compliance with ethical AI principles. This aligns with Salesforce's guidance to avoid protected characteristics in model training, thereby promoting equitable outcomes and maintaining trust.

Answer

Keep the fields but apply a post-processing adjustment to balance predictions across groups.

Answer

Replace the sensitive fields with anonymized versions that retain the same predictive power.

Answer

Proceed with deployment since Einstein Prediction Builder automatically handles bias mitigation.

Question 10

A Salesforce administrator at a retail company notices that an Einstein Discovery model predicting customer churn is performing poorly for customers over 65. An analysis shows the training dataset contains only 2% of records from that age group. Which action best addresses this ethical concern?

Accepted Answer

Collect additional representative data for the underrepresented age group and retrain the model.. The model's poor performance for older customers is a classic symptom of underrepresentation in training data. The ethical and effective fix is to improve data diversity by collecting more examples from that group and retraining. This reduces bias at the source and supports fairness, aligning with responsible AI principles of inclusive and representative data.

Answer

Remove the age field from the model's training dataset.

Answer

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

Answer

Deploy the model only for customers under 65 and use a separate manual process for older customers.

Question 11

A company uses Salesforce Einstein to analyze customer feedback and identify sentiment. They want to ensure the AI does not inadvertently discriminate against certain demographic groups. Which practice should they implement?

Accepted Answer

Regularly test the model's outputs for bias across different demographic groups.. To prevent discrimination, the company must proactively test for bias across demographic groups. This allows them to identify and address any disparities in the model's outputs. The other options either ignore the need for testing, use data improperly, or filter data in a way that hides issues.

Answer

Deploy the model without testing, as Einstein AI is pre-trained to be unbiased.

Answer

Limit the AI's analysis to only positive feedback to avoid negative sentiment.

Answer

Use a single, large dataset that includes all demographic groups without any preprocessing.

Question 12

A Salesforce partner is building an AI-powered chatbot for a healthcare client. The chatbot will handle patient inquiries and provide general health information. The partner wants to ensure the solution adheres to ethical AI principles. Which two actions should the partner take? (Choose two.)

Accepted Answer

Implement clear disclosures that the user is interacting with an AI, not a human.. The two correct actions are implementing clear AI disclosures and ensuring diverse training data. These align with Salesforce's responsible AI principles of transparency and fairness. Disclosures build trust, while diverse data prevents bias. Other options either overstep ethical boundaries, violate privacy, or negate the benefits of AI.

Answer

Allow the chatbot to provide specific medical diagnoses to save time.

Answer

Configure the chatbot to escalate all queries to a human agent immediately.

Answer

Store all patient conversations indefinitely for future model training without consent.

Question 13

A Salesforce developer is building an Einstein Bots-powered customer service chatbot. The bot will handle billing inquiries and may access customers' account details. Which design principle best ensures ethical handling of customer data?

Accepted Answer

Implement data minimization so the bot only retrieves information necessary to resolve the specific inquiry.. Data minimization ensures the chatbot only accesses information required to resolve the inquiry, reducing privacy risks and complying with ethical and legal standards. This principle is central to responsible AI, as it prevents unnecessary exposure of customer data. By limiting data retrieval, the organization demonstrates respect for customer privacy and builds trust.

Answer

Store all conversation logs indefinitely to improve future bot responses.

Answer

Use the Einstein Trust Layer to mask all customer data before the bot processes it.

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Enable the bot to access all customer records to provide comprehensive answers quickly.

Question 14

A Salesforce administrator is configuring Einstein Trust Layer for a customer service chatbot. The company operates in a region with strict data protection laws. The administrator wants to ensure that no personally identifiable information (PII) from chat transcripts is stored or used to train the underlying large language model. Which configuration should the administrator verify?

Accepted Answer

Confirm that the Einstein Trust Layer zero-retention setting is enabled so prompts and responses are not stored by the LLM provider.. The Einstein Trust Layer's zero-retention setting ensures that prompts and responses are not stored or used for training by the LLM provider. This directly satisfies the data protection requirement while allowing the chatbot to operate. Encryption at rest, DLP blocking, or private VPC deployment do not address the specific concern of external retention or training use.

Answer

Set up a Data Loss Prevention (DLP) policy to block PII from being included in prompts sent to the LLM.

Answer

Enable field-level encryption on all chat transcript objects to protect PII at rest within Salesforce.

Answer

Configure the LLM to run in a private instance within the customer's own virtual private cloud (VPC).

Question 15

A retail company uses Salesforce Einstein AI to generate product recommendations. The AI model was trained on purchase data that underrepresents rural customers, leading to poor recommendations for that demographic. Which ethical principle is most directly violated?

Accepted Answer

Fairness. The scenario describes an AI system that delivers inferior recommendations to rural customers because the training data underrepresents them. This is a classic example of algorithmic bias, which violates the ethical principle of fairness. Fairness demands that AI systems treat all demographic groups equitably, and when training data is not representative, the model can systematically disadvantage certain groups. Thus, fairness is the most directly violated principle.

Answer

Privacy

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Accountability

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Transparency