AI Associate Ethical Considerations of AI Practice Test 5 — 15 Questions

Question 1

Refer to the exhibit. A company uses an AI model for loan approvals. The error log shows a drift warning for a specific zip code, followed by a retraining failure due to insufficient data. What is the MOST ethical concern?

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

Accepted Answer

The model may produce biased outcomes for underserved groups. The drift warning indicates that the model's performance has degraded for a specific zip code, likely due to changes in the underlying data distribution. When retraining fails due to insufficient data, the model cannot adapt to these changes, which can lead to biased outcomes for underserved groups in that zip code. This is the most ethical concern because it directly impacts fairness and equity in automated decision-making.

Answer

The system failed to log the error

Answer

The system ignored the drift warning

Answer

The retraining process is too slow

Question 2

A global retail company deploys an AI-powered chatbot for customer service. The chatbot uses natural language processing to understand and respond to customer inquiries. After deployment, the company notices that the chatbot consistently provides less accurate and less helpful responses to customers from non-English-speaking regions, particularly those using dialects or slang. The company's data science team trained the model primarily on English-language customer service transcripts from the US and UK. The AI Ethics team has raised concerns about fairness and potential bias. The company wants to address this issue while maintaining overall performance and minimizing cost. Which action should the company take first?

Accepted Answer

Conduct a fairness audit using diverse test cases from multiple languages and dialects to quantify the disparity.. The first step in addressing potential bias in an AI system is to measure and quantify the disparity. Conducting a fairness audit with diverse test cases from multiple languages and dialects provides the data science team with a clear, empirical baseline of the model's performance gaps. This diagnostic step is essential before any remediation (like retraining or adding constraints) to ensure that subsequent actions are targeted and effective, avoiding wasted resources or unintended consequences.

Answer

Implement a fairness constraint in the model's loss function to penalize disparities across language groups.

Answer

Disable the chatbot for non-English languages and redirect those customers to human agents.

Answer

Collect more training data from all regions and retrain the model from scratch.

Question 3

A company uses an AI model to screen job applicants. They discover the model is rejecting candidates from a certain demographic at a higher rate. Which ethical principle is most clearly violated?

Accepted Answer

Fairness. Fairness in AI requires that models do not produce discriminatory outcomes across demographic groups. A hiring model that rejects one demographic at a higher rate exhibits disparate impact, which is the textbook definition of an unfair or biased model. The other principles — transparency, privacy, and accountability — address disclosure, data handling, and responsibility, not unequal treatment.

Answer

Transparency

Answer

Privacy

Answer

Accountability

Question 4

A Salesforce admin implements Einstein Bots for customer service. To ensure the bot does not use biased language, what should the admin do?

Accepted Answer

Review training data for representativeness. Bias in an Einstein Bot originates from the data used to train its language understanding models, not from the responses it returns at runtime. Reviewing training data for representativeness ensures the utterances, intents, and entities reflect a diverse and balanced set of user inputs, which directly reduces biased intent classification and response selection. This is the only option that addresses the root cause of biased language in the model.

Answer

Use only predefined responses

Answer

Disable the bot for sensitive topics

Answer

Monitor conversations regularly

Question 5

A company uses Einstein Prediction Builder to predict customer churn. They notice the model is less accurate for a certain segment. What is the best approach to mitigate bias?

Accepted Answer

Retrain with balanced data. Retraining with balanced data directly addresses the root cause of bias: an imbalanced training set where the model underperforms for a specific segment. By ensuring the segment is adequately represented, the model learns more equitable patterns, reducing bias without sacrificing overall accuracy. This aligns with ethical AI practices in Einstein Prediction Builder, where data quality and representation are critical for fair predictions.

Answer

Increase model complexity

Answer

Add more features

Answer

Remove the segment from training

Question 6

A user asks an AI assistant to generate content that may be offensive. What should the AI do?

Accepted Answer

Refuse and explain why. The AI should refuse the request and explain why, upholding ethical standards.

Answer

Ignore the request

Answer

Generate and report the user

Answer

Generate with a warning

Question 7

A company deploys an AI system that makes decisions about loan approvals. For transparency, what should they provide to applicants?

Accepted Answer

Explanation of factors considered. Transparency in AI-driven loan approvals requires providing applicants with an explanation of the factors considered in the decision, such as credit score, income, or debt-to-income ratio. This aligns with ethical AI principles like explainability and fairness, enabling applicants to understand and potentially contest the decision. Providing the full algorithm or training data would expose proprietary information and potentially violate data privacy regulations like GDPR.

Answer

The training data

Answer

The full algorithm

Answer

Confidence scores

Question 8

A Salesforce customer uses Einstein Sentiment Analysis to analyze customer feedback. They find the model is less accurate for non-English languages. What ethical concern does this raise?

Accepted Answer

Bias. When a sentiment model performs worse for non-English languages, it produces systematically less accurate results for those users, which is a form of bias (specifically, language or cultural bias). Bias in AI refers to systematic and unfair performance differences across groups, which is exactly what is described. Accountability, privacy, and security concern responsibility, data handling, and protection, none of which are the core issue here.

Answer

Accountability

Answer

Privacy

Answer

Security

Question 9

A developer creates a custom AI model using Salesforce's AI platform. They want to ensure the model is fair. What should they do first?

Accepted Answer

Collect diverse training data. Collecting diverse training data is the foundational step to ensure fairness in an AI model because it helps mitigate bias at the source. Without diverse data representing all relevant groups, the model may learn skewed patterns that lead to discriminatory outcomes, regardless of subsequent testing or monitoring.

Answer

Use a pre-trained model

Answer

Test on a small sample

Answer

Deploy and monitor

Question 10

An organization uses Einstein Discovery to analyze survey data. The model reveals a correlation between age and satisfaction. What is the responsible use of this insight?

Accepted Answer

Investigate causality before action. A correlation between age and satisfaction does not imply that age causes satisfaction. The responsible use is to investigate causality — through further analysis, controlled studies, or domain expertise — before taking action. Acting on a spurious correlation could lead to ineffective or harmful decisions.

Answer

Act on the correlation immediately

Answer

Discard the result

Answer

Publish the result as-is

Question 11

A company wants to ensure their AI model complies with ethical guidelines. Which TWO actions are essential? (Choose two.)

Accepted Answer

Provide human oversight. Option B is correct because human oversight is essential for ethical AI compliance: keeping a human in the loop allows review, intervention, and accountability for model outputs, especially in high-risk or ambiguous cases where automated judgments could cause harm. Option E is correct because documenting model decisions provides the audit trail, traceability, and explainability needed to demonstrate that the AI adheres to ethical guidelines and to support regulatory or internal reviews. The remaining options do not belong: avoiding transparency (A) directly undermines accountability and explainability, using the most complex model (C) does not improve ethics and can reduce interpretability, and automating all decisions (D) removes the human oversight that ethical compliance requires.

Answer

Avoid transparency

Answer

Use the most complex model

Answer

Automate all decisions

Question 12

An AI system used for recruitment has been found to be biased. Which THREE steps should be taken to address this? (Choose three.)

Accepted Answer

Audit the training data for bias. Option B is correct because auditing the training data for bias is the essential first diagnostic step: you must identify skewed distributions, under-representation, or historically discriminatory labels in the dataset before any remediation can be effective. Option C is correct because retraining the model with a balanced dataset directly addresses the root cause by correcting the class and demographic imbalances that produced the biased predictions. Option E is correct because monitoring outcomes for disparate impact provides ongoing post-deployment assurance, using metrics such as the 80% (four-fifths) rule to detect whether protected groups receive disproportionately unfavorable decisions over time. Option A does not belong because deploying the biased model unchanged perpetuates the harm and violates fairness and compliance obligations. Option D does not belong because simply removing demographic data (fairness through unawareness) does not eliminate bias, since correlated proxy features can still encode protected attributes and the model would then be unauditable for disparate impact.

Answer

Deploy the model without changes

Answer

Remove demographic data from the model

Question 13

A Salesforce administrator deploys an Einstein Bot. Which TWO ethical considerations should be addressed? (Choose two.)

Accepted Answer

The bot should disclose it is an AI. Option A is correct because ethical AI deployment requires transparency: an Einstein Bot should clearly disclose that it is an AI/automated agent so users know they are not interacting with a human, which supports informed consent and trust. Option E is correct because ethical bot design includes a clear escalation path to a human agent when the bot cannot resolve an issue or when the user requests it, preventing frustration and ensuring accountability for sensitive or complex cases. Option B is incorrect because deliberately mimicking a human without disclosure is deceptive and conflicts with transparency principles. Option C is incorrect because fully autonomous decision-making without human oversight is risky for ethical, compliance, and customer-care reasons; bots should operate within defined guardrails. Option D is incorrect because it is overly absolute: bots may legitimately collect personal data when necessary, provided it is done lawfully, with notice/consent, and in line with privacy requirements.

Answer

The bot should mimic a human

Answer

The bot should make decisions autonomously

Answer

The bot should not collect personal data

Question 14

A company uses Einstein Prediction Builder to score leads. The model systematically gives lower scores to leads from a particular geographic region, even though those leads often convert. Which action should the company take to address this ethical concern?

Accepted Answer

Retrain the model with a balanced dataset that includes more leads from the under-scored region.. The model is exhibiting geographic bias: it systematically under-scores leads from a region that actually converts well, meaning the training data likely underrepresents or mislabels that region. The correct ethical and technical remedy is to retrain with a balanced dataset that includes sufficient, correctly labeled examples from the under-scored region so the model learns the true conversion signal rather than a spurious geographic correlation. This addresses the root cause in the data rather than masking the symptom.

Answer

Switch to a different AI vendor.

Answer

Ignore the bias because the model is proprietary.

Answer

Remove the region field from the model entirely.

Question 15

An admin wants to use Einstein Reply Recommendations in Service Cloud. Which ethical consideration is most important to implement before enabling the feature?

Accepted Answer

Ensure customer data is anonymized.. Customer data privacy is paramount; anonymizing data ensures recommendations do not expose sensitive information.

Answer

Reduce the cost of agent training.

Answer

Maximize the number of recommendations.

Answer

Increase the speed of case resolution.