AI Associate Ethical Considerations of AI Practice Test 6 — 15 Questions

Question 1

A company uses Einstein GPT to generate email responses. They want to automatically audit generated responses for potentially harmful or biased language before sending. Which Salesforce feature should they use?

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

Accepted Answer

Einstein Trust Layer.. Einstein Trust Layer is the correct feature because it provides a governance layer that automatically audits AI-generated content for toxicity, bias, and harmful language before the email is sent. It intercepts the output from Einstein GPT and applies content safety filters, ensuring compliance with responsible AI practices without requiring manual review.

Answer

Permission Set.

Answer

Einstein Analytics.

Answer

Data Mask.

Question 2

Which two actions are consistent with Salesforce's ethical AI principles when deploying a custom AI model on Salesforce?

Accepted Answer

Document the model's intended use and limitations.. Option D is correct because Salesforce's ethical AI principles emphasize transparency and accountability, which require documenting a model's intended use, scope, and known limitations so stakeholders understand where and how the model should be applied. Option E is correct because providing a mechanism for users to challenge or appeal model decisions supports the principles of accountability, human oversight, and fairness, ensuring affected individuals have recourse when outcomes are disputed. Options A, B, and C are not consistent with these principles: restricting training to structured data is not a Salesforce ethical AI requirement, deploying a model to make decisions without human review undermines human-centered oversight, and optimizing solely for accuracy ignores other critical dimensions such as fairness, bias, and explainability.

Answer

Use only structured data for training.

Answer

Use the model to make decisions without human review.

Answer

Optimize for accuracy over all other metrics.

Question 3

A developer is creating a custom AI model on Salesforce. To ensure the model is fair across demographic groups, which activity should be included in the development process?

Accepted Answer

Bias testing using a diverse test dataset.. Bias testing using diverse datasets directly evaluates fairness across groups.

Answer

Feature selection using correlation matrix.

Answer

Cross-validation to avoid overfitting.

Answer

Hyperparameter tuning with grid search.

Question 4

A company receives a complaint that their Einstein Next Best Action recommendations are consistently suggesting different products based on the customer's ZIP code, leading to unequal access. What should the company do first?

Accepted Answer

Review the training data for geographic bias.. The first step in addressing biased AI recommendations is to investigate the root cause. Geographic bias in training data is a common source of unequal outcomes in machine learning models like Einstein Next Best Action. Reviewing the data allows the company to identify and mitigate the bias before taking any other action.

Answer

Contact Salesforce support for a refund.

Answer

Increase the number of recommendations shown.

Answer

Disable the recommendation engine immediately.

Question 5

A company wants to use Einstein Vision for product categorization. To ensure ethical use, they should:

Accepted Answer

Test the model for bias across different demographic groups.. Ethical use of a vision model for product categorization requires proactively checking that the model does not produce systematically different outcomes for different demographic groups. Testing for bias across demographic groups is the standard responsible-AI practice to detect and mitigate disparate impact before deployment. This directly addresses fairness, a core ethical requirement.

Answer

Avoid using any images that contain people.

Answer

Use only high-resolution images.

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Only use images from a single demographic.

Question 6

A Salesforce admin is configuring Einstein Search for an organization with users in multiple countries. Which ethical consideration is most important?

Accepted Answer

Language bias in the search results.. When Einstein Search serves users across multiple countries, the most important ethical consideration is language bias: search relevance models trained predominantly on one language or locale can return poorer or skewed results for other languages, effectively disadvantaging non-dominant-language users. This is a fairness and inclusion issue because search results influence what information users can find and act on. Ensuring balanced multilingual training data and evaluating relevance per locale is the ethical priority.

Answer

Stop word exclusion.

Answer

Query performance optimization.

Answer

Tokenization settings.

Question 7

A user asks an Einstein chatbot 'What is my current account balance?' The chatbot has been trained on transactions but is not supposed to reveal account data. Which ethical principle is at risk?

Accepted Answer

Privacy.. Revealing personal financial data violates the principle of privacy.

Answer

Accountability.

Answer

Transparency.

Answer

Fairness.

Question 8

A company uses Einstein Analytics to predict employee performance and identifies low-performing employees with high confidence. What is a potential ethical concern?

Accepted Answer

Invasion of employee privacy.. Einstein Analytics uses machine learning models to analyze employee data and predict performance. Identifying low-performing employees with high confidence raises ethical concerns about invasion of privacy because the model may rely on sensitive personal data (e.g., communication patterns, work hours, or behavioral metrics) without explicit employee consent or transparency. This violates principles of data minimization and informed consent, which are core to ethical AI frameworks.

Answer

High computational cost.

Answer

Difficulty in interpreting the model.

Answer

Overfitting on historical data.

Question 9

A company uses an AI model to screen job candidates. They discover the model is rejecting candidates from certain zip codes. What should they do first?

Accepted Answer

Audit training data for bias. Audit training data for bias. When a model shows discriminatory outcomes tied to a protected or proxy attribute like zip code, the first step is to investigate the root cause by auditing the training data for historical bias, underrepresentation, or skewed labels, since removing a feature or changing model architecture without understanding the source can mask rather than fix the problem. Auditing also establishes the evidence needed for remediation such as rebalancing data, applying fairness constraints, or using bias-detection tooling. Option A (increasing model complexity) would likely amplify the biased pattern rather than resolve it. Option B (adding more features) risks introducing additional proxy variables and worsening disparate impact. Option C (removing the zip code feature) may seem intuitive but is insufficient because correlated features can still encode the same bias, so it does not address the underlying data issue.

Answer

Increase model complexity

Answer

Add more features to the model

Answer

Remove zip code feature

Question 10

A Salesforce admin wants to use Einstein Prediction Builder to predict customer churn. What ethical consideration is most important?

Accepted Answer

Data privacy. Data privacy is the most important ethical consideration when using customer data for predictions like churn. Option A is wrong because cost of implementation is a business concern, not an ethical one. Option B is wrong because while model accuracy is important, it is a technical metric and not the primary ethical concern; privacy takes precedence. Option D is wrong because transparency of predictions is important for trust, but data privacy is more foundational when handling sensitive customer data.

Answer

Cost of implementation

Answer

Model accuracy

Answer

Transparency of predictions

Question 11

A company deploys an AI chatbot for customer service. After training on historical chats, the chatbot frequently gives incorrect answers to minority language queries. What is the likely cause?

Accepted Answer

Data imbalance. Data imbalance, because the historical chat data likely contains far fewer examples of minority language queries, so the model learns the majority language patterns well but has insufficient signal to answer minority language queries accurately. This skewed class/language distribution is a classic cause of poor performance on underrepresented groups. Underfitting (A) would show poor performance broadly, including on majority language queries, not specifically minority language ones. Lack of compute resources (C) affects training speed or model size, not language-specific accuracy. Overfitting (D) would typically cause poor generalization overall or memorization of training data, but the scenario points to a systematic gap tied to underrepresented language data.

Answer

Underfitting

Answer

Lack of compute resources

Answer

Overfitting

Question 12

An organization uses Einstein Recommendation Builder to suggest products. They want to ensure recommendations are fair across demographics. Which action should they take?

Accepted Answer

Regular bias audits. Regular bias audits (option C) are the right action because fairness across demographics requires ongoing measurement and monitoring of recommendation outcomes for disparate impact, not a one-time configuration change. Auditing lets the organization detect and correct skewed product suggestions before they harm specific groups. Option A is wrong because using only demographic features would amplify rather than reduce bias. Option B is wrong because removing all user data would destroy the personalization signal Einstein Recommendation Builder needs and does not by itself guarantee fairness. Option D is wrong because increasing model depth adds capacity and complexity but does not address or measure demographic fairness.

Answer

Use only demographic features

Answer

Remove all user data

Answer

Increase model depth

Question 13

A healthcare company uses AI to predict patient readmission rates. What is a critical ethical requirement?

Accepted Answer

Explanation of predictions to doctors. Explanation of predictions to doctors, because in a healthcare setting clinicians must be able to understand and justify AI-driven readmission risk scores before acting on them, which supports informed consent, accountability, and patient safety. Explainability is a core ethical requirement for high-stakes medical AI under frameworks such as the EU AI Act's high-risk classification and HIPAA-adjacent clinical governance. Low latency (B) is a performance concern, not an ethical mandate, and high precision (C) is a useful metric but does not by itself ensure ethical or transparent use. Using external data sources (D) is neither required nor necessarily ethical, since it can introduce privacy and consent risks rather than resolve them.

Answer

Low latency

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High precision

Answer

Use of external data sources

Question 14

A financial institution uses AI for loan approvals. They notice the model is denying loans to women more often. After retraining with balanced data, the disparity persists. What is the next best step?

Accepted Answer

Use adversarial debiasing technique. The next best step is D, use an adversarial debiasing technique, because the disparity persists even after retraining with balanced data, indicating that the bias is embedded in the model's learned representations rather than just the training distribution. Adversarial debiasing explicitly trains the model to make predictions that are invariant to the protected attribute (gender) by pitting a predictor against an adversary that tries to detect the protected attribute from the predictions, directly optimizing for fairness. Increasing regularization (A) or reducing model complexity (C) may reduce overfitting but do not specifically target gender-based disparity, and removing the gender feature (B) is insufficient because proxy variables can still encode gender information and perpetuate bias.

Answer

Increase model regularization

Answer

Remove the gender feature

Answer

Reduce model complexity

Question 15

A company uses AI to monitor employee productivity. Employees feel surveilled. What ethical principle is being violated?

Accepted Answer

Transparency. The scenario describes employees feeling surveilled by AI monitoring. The primary ethical principle implicated is privacy, because continuous AI surveillance can intrude on workers’ personal sphere and create a chilling effect on their behavior. The stem does not state that the monitoring was undisclosed or that employees were not informed, so Transparency (D) is only the best available answer if the principle is understood as the need for openness about AI monitoring; it is not justified by an unsupported claim that the surveillance was concealed. Inclusion (A) concerns equitable access and representation, accountability (B) concerns responsibility for AI outcomes, and safety (C) concerns preventing harm or misuse.

Answer

Inclusion

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Accountability

Answer

Safety