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CCNA Ethical Considerations of AI Questions

57 of 207 questions · Page 3/3 · Ethical Considerations of AI · Answers revealed

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151
MCQeasy

A company is deploying an AI-powered chatbot to handle customer service inquiries. The bot uses historical chat data for training. Which ethical consideration is MOST important to address before deployment?

A.Maximizing the chatbot's response accuracy
B.Obtaining consent from customers whose data is used for training
C.Ensuring the chatbot can handle high traffic volumes
D.Designing a human handoff protocol for complex issues
AnswerB

Using customer data requires informed consent and adherence to privacy laws.

Why this answer

The most critical ethical consideration is obtaining consent from customers whose historical chat data is used to train the chatbot. Under regulations like GDPR and CCPA, personal data (including chat transcripts) requires explicit consent for processing, especially when used to train an AI model. Deploying without consent violates data privacy laws and erodes user trust, regardless of the chatbot's technical performance.

Exam trap

Salesforce often tests the distinction between ethical obligations (like consent) and technical or operational features (like accuracy or scalability), leading candidates to confuse a performance metric with a compliance requirement.

How to eliminate wrong answers

Option A is wrong because maximizing response accuracy is a performance goal, not an ethical consideration; it does not address the legal and moral requirement for data consent. Option C is wrong because ensuring high traffic handling is a scalability and infrastructure concern, unrelated to the ethical principle of data privacy and consent. Option D is wrong because designing a human handoff protocol is a user experience and operational safety measure, not the primary ethical issue; it does not resolve the fundamental need for consent before using customer data for training.

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152
MCQmedium

A news aggregator app uses an AI algorithm to personalize the news feed for each user. The algorithm selects articles based on past clicks and reading time. Recently, a study reveals that the algorithm disproportionately shows sensational and polarizing news to users from certain political orientations, while showing more neutral content to others. The company's user engagement metrics have increased, but journalists express concern about reinforcing echo chambers and misinformation. The company wants to uphold ethical standards while keeping users engaged. What should they do?

A.Modify the algorithm to include diversity and reliability scores for news sources, promoting a balanced feed.
B.Allow users to manually select the types of news they want to see.
C.Show the same generic news feed to all users.
D.Continue with the current algorithm since it increases engagement.
AnswerA

This encourages exposure to different viewpoints and reduces the spread of unreliable content.

Why this answer

Option B is correct because incorporating diversity and reliability metrics into the algorithm balances engagement with ethical responsibilities. Option A ignores ethics. Option C eliminates personalization, hurting engagement.

Option D shifts responsibility to users, which may not be effective.

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153
MCQhard

A retail company uses Einstein to personalize product recommendations. The AI model is trained on customer purchase data that includes sensitive attributes like race and gender. The company wants to ensure ethical use. Which action would best address fairness concerns?

A.Remove race and gender fields from the training dataset
B.Obtain explicit consent from customers for data use
C.Add more demographic data to improve model accuracy
D.Randomize recommendations to ensure equal treatment
AnswerA

Removing protected attributes helps prevent direct discrimination.

Why this answer

The correct answer is A because removing sensitive attributes from training data mitigates direct discrimination. Option B is wrong because adding more data might not remove bias. Option C is wrong because randomizing recommendations reduces relevance and does not address bias.

Option D is wrong because obtaining additional consent does not fix bias in the model.

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154
MCQeasy

A customer service department uses an AI chatbot to handle common inquiries. Recently, customers have reported that the chatbot sometimes responds with offensive or inappropriate language. The company wants to uphold ethical standards. Which approach is the best practice?

A.Implement a filter to automatically block any offensive words in the chatbot's responses.
B.Limit the chatbot to only respond from a fixed set of predefined answers.
C.Implement human review of all chatbot responses that are flagged as potentially offensive.
D.Use a pre-trained language model from a trusted vendor to guarantee ethical behavior.
AnswerC

Human review ensures nuanced handling of sensitive content and allows model improvement based on feedback.

Why this answer

Option B is correct because human review for flagged responses provides a safety net and continuous improvement loop. Option A may block offensive words but can lead to false positives. Option C assumes pre-trained models are unbiased, which is not guaranteed.

Option D reduces functionality and customer experience.

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155
MCQmedium

A team is developing a chatbot for customer service. To ensure ethical AI, which practice should be incorporated?

A.Allow the chatbot to escalate to a human agent upon request.
B.Store all conversation data indefinitely for analysis.
C.Use a single data source for training to avoid inconsistency.
D.Design the chatbot to mimic human emotions perfectly.
AnswerA

Human escalation provides oversight and user control.

Why this answer

Option A is correct because human escalation supports accountability and user control. Option B is wrong because indefinite data storage violates privacy. Option C is wrong because mimicking emotions may deceive users.

Option D is wrong because a single data source may introduce bias.

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156
MCQeasy

A nonprofit uses an AI system to allocate resources to communities in need. The system uses historical data which shows that certain neighborhoods have lower service usage. What ethical risk should be considered?

A.The system may violate data minimization principles
B.The system cannot be held accountable for decisions
C.The system lacks explainability
D.The system may perpetuate historical inequities
AnswerD

Using biased historical data can reinforce past discrimination.

Why this answer

Option D is correct because the AI system uses historical data that reflects lower service usage in certain neighborhoods. If that historical data is biased due to past inequities (e.g., redlining, underinvestment, or systemic discrimination), the model will learn and amplify those patterns, leading to unfair resource allocation that perpetuates historical disadvantages. This is a classic case of algorithmic bias where the training data encodes societal biases, and the model's predictions reinforce them.

Exam trap

Salesforce often tests the distinction between bias from training data (Option D) versus model explainability (Option C), so candidates mistakenly pick 'lack of explainability' when the real issue is that the model is accurately learning from flawed historical data.

How to eliminate wrong answers

Option A is wrong because data minimization principles (from GDPR and privacy frameworks) concern collecting only necessary personal data, not the fairness of outcomes; the risk here is about bias, not data collection scope. Option B is wrong because AI systems can be held accountable through governance frameworks, audit trails, and human oversight; the statement confuses technical accountability with legal liability. Option C is wrong because while lack of explainability (black-box models) is a concern, the primary ethical risk in this scenario is that the system will replicate historical bias from the training data, not that its decisions are opaque.

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157
MCQmedium

An AI Associate is asked to build a model that predicts employee performance. The dataset includes gender, department, and tenure. Which practice could introduce ethical risk?

A.Evaluating model performance across different groups.
B.Excluding gender from the model features.
C.Documenting model limitations and assumptions.
D.Including gender to improve model accuracy.
AnswerD

Using protected attributes can lead to biased outcomes.

Why this answer

Option D is correct because including gender as a feature in a predictive model for employee performance can introduce bias and lead to unfair or discriminatory outcomes. Even if the model's accuracy improves, using protected attributes like gender may violate ethical guidelines and regulations such as GDPR or anti-discrimination laws, as it could perpetuate historical biases or result in disparate impact.

Exam trap

Salesforce often tests the misconception that including more features always improves model performance, without considering the ethical implications of using protected attributes like gender.

How to eliminate wrong answers

Option A is wrong because evaluating model performance across different groups is a standard fairness practice, such as measuring demographic parity or equal opportunity, and helps identify bias rather than introducing ethical risk. Option B is wrong because excluding gender from the model features is a common bias mitigation technique, often called 'fairness through unawareness,' which reduces the risk of direct discrimination. Option C is wrong because documenting model limitations and assumptions is a responsible AI practice that promotes transparency and accountability, not an ethical risk.

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158
Multi-Selecteasy

A company is developing an AI system to screen job applicants. Which TWO practices are essential for ethical AI in hiring?

Select 2 answers
A.Using all available data including demographic details
B.Auditing the model for bias against protected groups
C.Relying solely on AI for final decisions
D.Maximizing processing speed
E.Providing candidates with explanation of decisions
AnswersB, E

Bias auditing is crucial to ensure fairness.

Why this answer

Options B and D are essential: auditing for bias (B) and ensuring transparency (D). Option A is wrong because speed is not ethical. Option C is wrong as using all data may include biased data.

Option E is wrong because sole reliance on AI is unethical without human oversight.

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159
MCQmedium

A company uses an AI model to automate customer service responses. A customer receives an incorrect response that results in a financial loss. Who is primarily accountable for this error?

A.The AI model
B.The developer who built the model
C.The organization that deployed the AI
D.The customer who received the response
AnswerC

Organizations are accountable for the systems they deploy.

Why this answer

The correct answer is B because the organization deploying the AI is accountable for its outcomes. Option A is wrong because the AI itself is not accountable. Option C is wrong because the developer may share responsibility, but ultimate accountability lies with the organization.

Option D is wrong because the customer is not responsible.

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160
MCQhard

Refer to the exhibit. An AI loan approval policy is defined as a JSON rule set. Which ethical issue is most prominent based on this policy?

A.Use of irrelevant attributes like income and credit score
B.Insufficient accuracy due to simple rules
C.Potential for geographic discrimination due to zip code condition
D.Lack of transparency in decision-making
AnswerC

Zip code can be a proxy for race or socioeconomic status, leading to discrimination.

Why this answer

Option C is correct: The use of zip_code as an approval condition can lead to geographic discrimination (redlining). Option A is wrong because the rules are transparent (explicitly shown). Option B is wrong because income and credit score may be relevant, but zip code is problematic.

Option D is wrong because the rules are defined, but accuracy is not directly addressed.

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161
MCQhard

A financial services firm uses Einstein Next Best Action to offer credit products. The model recommends high-interest loans more often to minority groups. The AI Associate must mitigate this. What is the most effective approach?

A.Remove the model and use a rule-based system.
B.Use SHAP values to explain predictions.
C.Apply post-processing fairness adjustments to the recommendations.
D.Add a disclaimer that recommendations may be biased.
AnswerC

This can equalize outcomes without full retraining.

Why this answer

Option C is correct because post-processing fairness adjustments directly modify the model's output to enforce demographic parity or equal opportunity, reducing biased recommendations without retraining the model. This approach is practical when the firm cannot easily change the underlying training data or model architecture, and it allows the AI Associate to intervene at the decision point to ensure fair lending practices.

Exam trap

The trap here is that candidates confuse explainability (SHAP values) with mitigation, thinking that understanding why bias occurs is sufficient to fix it, when in fact only direct adjustments to the model's output can change the biased recommendations.

How to eliminate wrong answers

Option A is wrong because removing the model and using a rule-based system would discard the predictive power of machine learning and likely still encode human biases in the rules, failing to address the root cause of bias. Option B is wrong because SHAP values only explain why a model made a particular prediction; they do not change the model's behavior or mitigate bias in the recommendations. Option D is wrong because adding a disclaimer does not alter the biased outcomes; it merely informs users of potential bias, which does not satisfy regulatory or ethical obligations to ensure fair treatment.

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162
MCQhard

A financial institution uses an AI model to approve credit. The model shows disparate impact against a protected group. Under Salesforce's ethical AI principles, what is the most appropriate action?

A.Discontinue the model immediately.
B.Increase the model's decision threshold for all applicants.
C.Publish the model's predictions publicly for scrutiny.
D.Apply a bias mitigation technique such as reweighing.
AnswerD

Bias mitigation techniques directly address disparate impact.

Why this answer

Option B is correct because bias mitigation techniques like reweighing address disparate impact while maintaining functionality. Option A is wrong because discontinuing the model may cause operational disruption and is not necessary if bias can be mitigated. Option C is wrong because adjusting the threshold may not address the root cause.

Option D is wrong because publicizing predictions may violate privacy.

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163
MCQeasy

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?

A.Bias
B.Accountability
C.Privacy
D.Security
AnswerA

Correct. The model is biased against non-English languages.

Why this answer

The varying accuracy across languages indicates bias in the model, which is a fairness concern.

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164
MCQhard

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?

A.Increase model complexity
B.Add more features
C.Remove the segment from training
D.Retrain with balanced data
AnswerD

Correct. Balanced data helps the model perform consistently across segments.

Why this answer

Option D is correct because 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.

Exam trap

Salesforce often tests the misconception that bias is a technical problem solvable by adding complexity or features, when in fact it is a data representation issue requiring balanced training data.

How to eliminate wrong answers

Option A is wrong because increasing model complexity (e.g., adding more layers or interactions) can exacerbate overfitting and may amplify existing biases rather than mitigate them, especially if the biased segment is underrepresented. Option B is wrong because adding more features does not guarantee bias reduction; irrelevant or proxy features can introduce new biases or reinforce existing ones, and the core issue is data imbalance, not feature insufficiency. Option C is wrong because removing the segment from training eliminates the model's ability to predict for that segment entirely, which is a form of exclusion bias and violates ethical AI principles of fairness and inclusivity.

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165
MCQmedium

An HR department uses an AI tool to screen resumes for a software engineering position. The tool was trained on resumes of past successful hires, who were predominantly male. The tool has been in use for three months, during which only 10% of candidates shortlisted for interviews are female, even though 40% of applicants are female. The hiring managers are satisfied with the quality of candidates shortlisted, as most perform well in interviews. However, the company's diversity and inclusion officer raises an ethical concern. What should the company do to address this bias?

A.Retrain the model with a balanced dataset that includes more female candidates and remove gender-correlated features.
B.Use the AI tool for initial screening but allow candidates to appeal the decision.
C.Continue using the current tool since it selects high-quality candidates.
D.Manually review all resumes without using the AI tool.
AnswerA

This reduces bias by ensuring the model learns from diverse examples and avoids proxy discrimination.

Why this answer

Option B is correct because retraining with a balanced dataset and using gender-blind features directly addresses the source of bias. Option A perpetuates the bias. Option C is too labor-intensive for high volume.

Option D still relies on the biased model for initial screening.

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166
Multi-Selecthard

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

Select 3 answers
A.Deploy the model without changes
B.Audit the training data for bias
C.Retrain the model with a balanced dataset
D.Remove demographic data from the model
E.Monitor outcomes for disparate impact
AnswersB, C, E

Correct. Auditing identifies sources of bias.

Why this answer

Auditing training data, retraining with balanced data, and monitoring outcomes are essential corrective actions.

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167
Multi-Selecteasy

A company is deploying Einstein Vision for product quality inspection. To ensure ethical use, which TWO practices should they adopt? (Choose two.)

Select 2 answers
A.Test the model for bias across different product types and lighting conditions
B.Keep the model's decision-making process proprietary to protect intellectual property
C.Deploy the model without human oversight to maximize efficiency
D.Provide clear documentation on the model's limitations and expected accuracy
E.Only use training images from a single supplier to maintain consistency
AnswersA, D

Bias testing ensures fair performance.

Why this answer

Option A (Test the model for bias across different product types) is correct because bias testing is essential. Option C (Provide clear documentation on the model's limitations) is correct for transparency. Option B (Use the model without human oversight) violates accountability.

Option D (Only use images from one supplier) may introduce bias. Option E (Keep the model's decisions secret) violates transparency.

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168
MCQmedium

A global e-commerce company deploys Einstein Bots in multiple countries. The bot uses natural language processing to handle customer returns. In one region, customers frequently complain that the bot does not understand their local dialect and incorrectly rejects valid returns. The company wants to maintain consistent customer experience while respecting regional diversity. The bot's language model was trained mainly on English data from the US and UK. The AI ethics board is concerned about fairness and transparency. They consider four options: (A) use a single, centrally-trained model with fallback to human agents for non-English queries, (B) deploy separate models fine-tuned on each dialect but with centralized monitoring, (C) disable the bot in regions with dialect issues, (D) use a translation layer to convert all inputs to English before processing. What is the best ethical approach?

A.Use a single centrally-trained model with fallback to human agents for non-English queries
B.Use a translation layer to convert all inputs to English before processing
C.Disable the bot in regions with dialect issues
D.Deploy separate models fine-tuned on each dialect with centralized monitoring
AnswerD

Fine-tuning respects linguistic diversity and central monitoring ensures consistent ethics.

Why this answer

Option B is correct because fine-tuning on local dialects improves accuracy and fairness, while centralized monitoring ensures oversight. Option A is wrong: fallback to human agents is good but still may cause delays and dissatisfaction; the model itself is not inclusive. Option C is wrong: disabling the bot denies service and is not inclusive.

Option D is wrong: translation may lose nuance and introduce errors.

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169
Multi-Selectmedium

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

Select 2 answers
A.Use only structured data for training.
B.Use the model to make decisions without human review.
C.Optimize for accuracy over all other metrics.
D.Document the model's intended use and limitations.
E.Provide a mechanism for users to challenge model decisions.
AnswersD, E

Transparency is a core ethical principle.

Why this answer

Documenting intended use (transparency) and providing a challenge mechanism (accountability) align with ethical AI principles.

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170
Multi-Selecteasy

Which THREE are core principles in Salesforce's AI ethics framework?

Select 3 answers
A.Accuracy
B.Fairness
C.Profitability
D.Transparency
E.Privacy
AnswersB, D, E

Core principle.

Why this answer

Option A is correct because privacy is a core principle. Option C is correct because fairness is a core principle. Option D is correct because transparency is a core principle.

Option B is wrong while important, accuracy is not explicitly a core ethical principle in Salesforce's framework; they focus on fairness, transparency, accountability, privacy, and safety. Option E is wrong profitability is not an ethical principle.

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171
MCQmedium

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?

A.Use a pre-trained model
B.Test on a small sample
C.Collect diverse training data
D.Deploy and monitor
AnswerC

Correct. Diverse data helps prevent systemic bias.

Why this answer

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.

Exam trap

Salesforce often tests the misconception that fairness can be achieved through post-hoc actions like testing or monitoring, rather than through proactive data collection, leading candidates to choose 'Test on a small sample' or 'Deploy and monitor' as the first step.

How to eliminate wrong answers

Option A is wrong because using a pre-trained model does not guarantee fairness; the pre-trained model may itself contain biases from its original training data, and the developer still needs to ensure the data used for fine-tuning or adaptation is diverse. Option B is wrong because testing on a small sample is insufficient to detect systemic bias; a small sample may not capture the full range of demographic or behavioral variations, leading to false confidence in fairness. Option D is wrong because deploying and monitoring comes after the model is built; without first ensuring diverse training data, monitoring will only detect issues after harm may have occurred, rather than preventing them at the source.

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172
MCQeasy

Refer to the exhibit. A developer receives this fairness check error. What is the most likely cause?

A.The model has higher false positive and false negative rates for Group B.
B.The error is due to insufficient training data for Group A.
C.The model is overfitting.
D.The recommendation suggests reweighting, so the model is already fair.
AnswerA

The higher rates for Group B indicate bias.

Why this answer

Option B is correct because the exhibit clearly shows significantly higher false positive and false negative rates for Group B, indicating the model treats groups differently. Option A is wrong because overfitting is not indicated by these metrics. Option C is wrong because the recommendation is to fix the issue, not that the model is already fair.

Option D is wrong because Group A has lower rates, suggesting Group B's data may be insufficient, but the cause is the disparity itself.

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173
MCQmedium

A company's Einstein Sentiment model is used to flag negative customer feedback. The model was trained on English reviews only. When deployed globally, it misclassifies positive reviews in Spanish as negative. What is the primary ethical concern?

A.The model is not interpretable.
B.The model has low accuracy for Spanish reviews.
C.The model is unfair to Spanish-speaking customers.
D.The model violates privacy regulations.
AnswerC

Lack of representation leads to unfair treatment.

Why this answer

The primary ethical concern is fairness: the model was trained exclusively on English reviews, so it systematically misclassifies Spanish positive feedback as negative. This creates a disparate impact on Spanish-speaking customers, violating the principle of algorithmic fairness. The issue is not just low accuracy but an unjust bias that disadvantages a specific linguistic group.

Exam trap

Salesforce often tests the distinction between a model's technical flaw (low accuracy) and the ethical principle it violates (fairness), tricking candidates into picking the symptom over the root ethical concern.

How to eliminate wrong answers

Option A is wrong because interpretability (explainability) is a separate concern; the model could be interpretable yet still unfair. Option B is wrong because low accuracy is a symptom, not the primary ethical concern—the core issue is the unfair bias against Spanish speakers, not mere performance degradation. Option D is wrong because the scenario involves no personal data collection or processing that would violate privacy regulations like GDPR or CCPA; the model only analyzes review text for sentiment.

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174
MCQmedium

A company deployed an AI chatbot for customer service. After a week, they receive complaints that the chatbot responds differently based on customer accent. The ethical issue is most likely due to:

A.Lack of personality in the chatbot responses
B.Insufficient computational resources allocated to the chatbot
C.Poor user interface design
D.Bias in the training data leading to discriminatory behavior
AnswerD

Bias in data is a common source of unfair AI behavior.

Why this answer

The chatbot's differing responses based on accent indicate bias in the training data or model. Option D (bias in training data leading to discriminatory behavior) is correct because AI models learn from data, and if the data contains accents correlated with negative outcomes, the model perpetuates that. Option A (insufficient compute resources) is unrelated.

Option B (lack of chatbot personality) is not ethical. Option C (user interface design) is not the cause.

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175
MCQeasy

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

A.Cost of implementation
B.Model accuracy
C.Data privacy
D.Transparency of predictions
AnswerC

Protecting customer data privacy is a core ethical requirement.

Why this answer

Option C is correct because data privacy is paramount when using customer data for predictions. Option A is wrong while accuracy is important, but privacy is a foundational ethical concern. Option B is wrong because transparency is important but not as fundamental as privacy in this context.

Option D is wrong because cost is a business, not ethical, concern.

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176
MCQeasy

A government agency uses an AI system to allocate resources for public services such as healthcare and education. The system is designed to optimize overall efficiency based on historical usage data. After deployment, it becomes clear that underserved regions with less historical data receive significantly less funding than well-served regions. The agency's mission is to promote equity. The system's performance metrics show high efficiency, but community leaders protest the unfair distribution. What should the agency do?

A.Abandon the AI system and use a manual, rule-based allocation system.
B.Redesign the system to include fairness constraints that ensure minimum resource levels for underserved regions.
C.Collect more historical data from underserved regions before making adjustments.
D.Continue using the system as is, since it maximizes efficiency.
AnswerB

Fairness constraints balance efficiency with equity, meeting both goals.

Why this answer

Option B is correct because incorporating fairness constraints ensures equitable distribution while still using AI to optimize. Option A ignores the fairness issue. Option C is good but may not be sufficient if the model still biases against underrepresented areas.

Option D reverts to a less efficient system.

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177
MCQmedium

An AI model for predicting employee performance is found to have a higher false positive rate for women than for men. What is the best course of action?

A.Investigate the cause and retrain the model to reduce bias
B.Lower the decision threshold for women to equalize false positive rates
C.Proceed with deployment because the overall accuracy is acceptable
D.Use the model but require manual review for all female candidates
AnswerA

Retraining with fairness constraints mitigates bias.

Why this answer

Option A is correct because a higher false positive rate for women indicates the model has learned biased patterns from the training data, likely due to imbalanced or skewed historical data. Investigating the cause—such as examining feature correlations, data distribution, and model architecture—allows for targeted retraining (e.g., reweighting, adversarial debiasing, or fairness constraints) to reduce bias without sacrificing overall performance. This aligns with ethical AI principles and regulatory expectations, ensuring the model is fair across demographic groups.

Exam trap

Salesforce often tests the misconception that adjusting thresholds or adding manual review can fix bias, when in fact these are superficial patches that do not address the root cause in the model's training data or architecture.

How to eliminate wrong answers

Option B is wrong because lowering the decision threshold for women artificially equalizes false positive rates but does not address the underlying bias; it may increase false negatives for women or degrade overall model calibration, leading to inconsistent and unfair outcomes. Option C is wrong because proceeding with deployment despite known bias violates fairness standards and can lead to discriminatory practices, legal liability, and reputational damage, even if overall accuracy is acceptable. Option D is wrong because requiring manual review for all female candidates introduces a separate, potentially biased human-in-the-loop process that is inefficient, costly, and does not fix the model's bias; it also creates a two-tier system that may still result in unfair treatment.

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178
MCQmedium

Refer to the exhibit. A Salesforce developer configures the Einstein Trust Layer as shown. What is the primary purpose of enabling data masking?

A.To improve the accuracy of sentiment analysis.
B.To reduce latency of the AI response.
C.To anonymize personally identifiable information (PII) in the model output.
D.To comply with Salesforce's service-level agreement.
AnswerC

The maskFields specify PII types to be hidden.

Why this answer

Enabling data masking in the Einstein Trust Layer ensures that personally identifiable information (PII) is anonymized before the model output is returned to the user. This protects sensitive data from exposure in AI-generated responses, which is a core requirement for privacy compliance and responsible AI use.

Exam trap

Salesforce often tests the distinction between data masking (which protects output privacy) and data encryption (which protects data in transit or at rest), leading candidates to confuse masking with security controls that affect latency or compliance with SLAs.

How to eliminate wrong answers

Option A is wrong because data masking does not improve sentiment analysis accuracy; it removes or obscures PII, which could actually reduce context for sentiment models if not handled carefully. Option B is wrong because data masking adds processing overhead to scan and redact PII, which may increase latency rather than reduce it. Option D is wrong because while data masking helps meet privacy regulations, it is not specifically tied to Salesforce's service-level agreement (SLA), which covers uptime and performance, not data anonymization.

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179
MCQeasy

Refer to the exhibit. This JSON snippet is from the Einstein Trust Layer configuration. What is the purpose of this configuration?

A.To detect biased predictions based on gender and race
B.To block all predictions involving gender or race
C.To anonymize gender and race data
D.To remove gender and race from the model
AnswerA

Correct. The bias detection feature checks for disparities along these attributes.

Why this answer

The configuration enables bias detection on the specified sensitive attributes (gender and race).

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180
MCQmedium

A healthcare provider uses an AI model to predict patient readmission risk. The model is trained on historical data that underrepresents minority populations. What is the MOST significant ethical risk?

A.The model may overfit to the majority population
B.The model cannot scale to real-time predictions
C.The model may produce biased predictions against minorities
D.The model lacks explainability
AnswerC

Underrepresentation in training data causes algorithmic bias, an ethical risk.

Why this answer

Option C is correct because training on historical data that underrepresents minority populations leads to a model that has insufficient examples to learn patterns for those groups, resulting in biased predictions that systematically disadvantage minorities. This is a direct violation of fairness in AI ethics, as the model's outputs will be less accurate or equitable for underrepresented groups, potentially causing harm in critical healthcare decisions like readmission risk assessment.

Exam trap

Salesforce often tests the distinction between a technical symptom (like overfitting) and the core ethical consequence (like biased predictions), so candidates may incorrectly choose overfitting as the most significant risk instead of recognizing that the ethical harm to minorities is the primary concern.

How to eliminate wrong answers

Option A is wrong because overfitting to the majority population is a symptom of the data imbalance, but the most significant ethical risk is the resulting bias and harm to minority groups, not the overfitting itself. Option B is wrong because the ability to scale to real-time predictions is a performance or deployment concern, not an ethical risk; the model could still be deployed in real-time while producing biased outputs. Option D is wrong because while lack of explainability can be an ethical concern, it is not the most significant risk here; the primary issue is the biased predictions caused by underrepresented data, which can occur even if the model is fully explainable.

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181
MCQhard

A healthcare organization uses Salesforce to manage patient records. They want to deploy an AI system that predicts patient readmission risk. Which practice BEST ensures ethical use of patient data?

A.Use the model to deny high-risk patients coverage
B.Focus solely on model accuracy regardless of data source
C.Obtain explicit patient consent and anonymize data for training
D.Deploy a third-party AI tool without reviewing its data practices
AnswerC

Consent and anonymization protect patient privacy and comply with regulations.

Why this answer

Option B is correct because patient consent and data anonymization are key to ethical AI in healthcare. Option A is wrong because predictions may still lead to discrimination. Option C is wrong because accuracy alone doesn't guarantee ethical use.

Option D is wrong as third-party tools may not respect privacy laws.

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182
MCQhard

Refer to the exhibit. An AI model's accuracy is shown for four demographic groups. Which group should be investigated for potential bias?

A.Group Beta
B.Group Alpha
C.Group Gamma
D.Group Delta
AnswerA

Correct. The low accuracy suggests bias issues.

Why this answer

Group Beta has significantly lower accuracy, indicating possible bias or underperformance.

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183
Multi-Selecteasy

Which TWO practices help ensure accountability in AI systems?

Select 2 answers
A.Blame the AI system for mistakes to protect employees.
B.Assign a human owner for each AI system.
C.Automate all decisions to eliminate human error.
D.Implement logging and auditing of model decisions.
E.Open-source the model code to share responsibility.
AnswersB, D

Human ownership ensures responsibility.

Why this answer

Option B is correct because assigning a human owner for each AI system establishes clear accountability, ensuring that a specific individual is responsible for the system's behavior, decisions, and compliance with ethical guidelines. This practice aligns with the principle of human oversight, which is critical for maintaining trust and addressing failures in AI systems.

Exam trap

Salesforce often tests the distinction between technical transparency (like open-sourcing code) and operational accountability (like assigning a human owner), leading candidates to mistakenly choose open-sourcing as a sufficient accountability practice.

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184
MCQmedium

Refer to the exhibit. An AI model audit shows performance differences across demographic groups. Which ethical concern is most critical?

A.Privacy: the data includes sensitive attributes
B.Accountability: the audit was not independent
C.Transparency: the model's overall accuracy is too low
D.Fairness: the model performs worse for the minority group
AnswerD

Biased performance across groups is a fairness issue.

Why this answer

Option A is correct: The significant disparity in accuracy and error rates indicates bias, which violates fairness. Option B is wrong because overall accuracy is high, but group fairness is lacking. Option C is wrong because accountability is about responsibility, but the exhibit directly shows unfairness.

Option D is wrong because privacy is not directly indicated.

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185
MCQeasy

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?

A.Continue using the model because 92% accuracy is acceptable and the bias is not significant.
B.Retrain the model using a balanced dataset that includes equal representation from all genders and implement ongoing fairness monitoring.
C.Replace the current AI tool with a different vendor's tool without further analysis.
D.Manually adjust the scoring algorithm to give preference to female candidates to balance the outcome.
AnswerB

This directly addresses the bias by ensuring the training data is representative and includes measures to monitor fairness.

Why this answer

Option B is correct because retraining with a balanced dataset addresses the root cause of bias, and ongoing monitoring ensures fairness over time. Option A is incorrect because ignoring ethical concerns for accuracy is unacceptable. Option C is incorrect because switching vendors without understanding the bias may not solve the issue.

Option D is incorrect because manually adjusting scores introduces reverse discrimination and is unethical.

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186
MCQhard

Refer to the exhibit. The fairness evaluation shows a disparate impact of 0.85, equal opportunity difference of 0.12, and demographic parity difference of 0.18. Which fairness thresholds are violated?

A.Demographic parity only.
B.Equal opportunity only.
C.Equal opportunity and demographic parity.
D.Disparate impact only.
AnswerC

Both exceed their thresholds.

Why this answer

The correct answer is C because the fairness evaluation shows violations of both equal opportunity and demographic parity thresholds. The equal opportunity difference of 0.12 exceeds the commonly accepted threshold of 0.1, and the demographic parity difference of 0.18 exceeds the typical threshold of 0.1. Disparate impact of 0.85 is within the acceptable range (typically 0.8 to 1.25), so it is not violated.

Exam trap

Salesforce often tests the misconception that disparate impact is the only fairness metric that matters, but the trap here is that multiple fairness thresholds can be violated simultaneously, and candidates must check each metric against its specific threshold rather than assuming a single violation.

How to eliminate wrong answers

Option A is wrong because demographic parity difference of 0.18 exceeds the 0.1 threshold, but equal opportunity difference of 0.12 also exceeds its 0.1 threshold, so both are violated, not just demographic parity. Option B is wrong because equal opportunity difference of 0.12 exceeds the 0.1 threshold, but demographic parity difference of 0.18 also exceeds its threshold, so both are violated, not just equal opportunity. Option D is wrong because disparate impact of 0.85 falls within the acceptable range of 0.8 to 1.25 (or 80% to 125% rule), so it is not violated.

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187
Multi-Selectmedium

To comply with Salesforce's AI ethics principles when using Einstein Bots, which two practices should be implemented?

Select 2 answers
A.Allow users to escalate to a human agent.
B.Use the bot to make all customer decisions autonomously.
C.Store all conversation transcripts indefinitely.
D.Disclose that the user is interacting with a bot.
E.Minimize data collection to only what is necessary.
AnswersA, D

Human oversight ensures accountability.

Why this answer

Disclosing bot identity (transparency) and allowing human escalation (accountability) are key ethical practices.

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188
MCQeasy

A company uses an AI chatbot that automatically responds to customer service inquiries. When a customer questions the bot's response, there is no mechanism for the customer to appeal or speak to a human. What ethical principle is being violated?

A.Privacy
B.Transparency
C.Accountability
D.Fairness
AnswerC

Accountability requires human oversight and the ability to override AI decisions.

Why this answer

Option D is correct because human oversight and the ability to escalate to a human is essential for accountability. Option A is wrong because privacy may be fine. Option B is wrong because the bot may be accurate but lack fairness due to no recourse.

Option C is wrong because the bot may be transparent about what it does.

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189
Multi-Selecteasy

Which TWO actions promote transparency in AI decision-making?

Select 2 answers
A.Use black-box models that maximize accuracy.
B.Document the model's limitations and potential biases.
C.Keep the AI algorithms proprietary to protect intellectual property.
D.Only share the final results of the AI system.
E.Provide clear explanations for significant AI decisions.
AnswersB, E

Transparency includes being honest about what the model cannot do.

Why this answer

Options A and B are correct. Providing explanations and documenting limitations help users understand and trust the AI. Keeping algorithms proprietary (C) reduces transparency.

Black-box models (D) are inherently opaque. Only sharing results (E) hides the process.

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190
MCQeasy

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?

A.Switch to a different AI vendor.
B.Ignore the bias because the model is proprietary.
C.Retrain the model with a balanced dataset that includes more leads from the under-scored region.
D.Remove the region field from the model entirely.
AnswerC

Retraining with balanced data directly addresses the bias by giving equal representation.

Why this answer

Retraining with a balanced dataset helps mitigate bias by ensuring the model learns from a representative sample, aligning with fairness principles.

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191
MCQeasy

A company uses Einstein Prediction Builder to recommend products. They notice the model often recommends high-priced items to users in affluent areas, potentially excluding others. What should the AI Associate do first?

A.Remove the model from production immediately.
B.Ignore the issue because the model predictions are accurate overall.
C.Add more features about customer income.
D.Check the training data for representation and bias.
AnswerD

Addressing data bias is the first step per Salesforce ethical AI guidelines.

Why this answer

The correct first step is to check the training data for representation and bias because the model's tendency to recommend high-priced items to affluent areas suggests the training data may be skewed or contain historical biases. Einstein Prediction Builder relies on historical data to learn patterns, and if the data over-represents affluent users or under-represents others, the model will perpetuate those biases. Auditing the data for fairness and representation is the foundational step before any remediation, as per responsible AI practices.

Exam trap

Salesforce often tests the misconception that adding more features or immediately removing the model is the right fix, when the correct first step is always to audit the training data for bias and representation.

How to eliminate wrong answers

Option A is wrong because removing the model from production immediately is a drastic, premature action without first diagnosing the root cause; the issue may be fixable through data adjustments or retraining. Option B is wrong because ignoring the issue violates ethical AI principles and could lead to discriminatory outcomes, even if overall accuracy is high, as fairness is a separate metric from accuracy. Option C is wrong because adding more features about customer income could exacerbate bias or introduce privacy concerns, and the core problem is likely in the existing data distribution, not a lack of features.

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192
Multi-Selectmedium

Which TWO actions align with ethical AI practices in Salesforce?

Select 2 answers
A.Using only internal data for training
B.Automating all decisions without human review
C.Monitoring models for bias
D.Using customer data without explicit consent
E.Providing explanations for AI predictions
AnswersC, E

Bias monitoring ensures fairness over time.

Why this answer

Options B and C are correct. Providing explanations for AI predictions ensures transparency, and monitoring models for bias is a core ethical practice. Option A is wrong because using data without consent violates privacy.

Option D is wrong because automating all decisions removes human oversight. Option E is wrong because restricting to internal data may limit inclusivity.

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193
MCQhard

A bank uses Einstein to approve loan applications. The model is trained on data that includes zip codes. Analysis shows that applicants from low-income zip codes are disproportionately rejected, even when their credit profiles are similar. What is the most likely ethical issue?

A.Privacy violation due to zip code use
B.Use of a proxy for protected attributes
C.Lack of transparency in decision-making
D.Unreliable model performance
AnswerB

Zip code can proxy for race/income, leading to unfair discrimination.

Why this answer

The correct answer is C because zip code can be a proxy for race or socioeconomic status, leading to proxy discrimination. Option A is wrong because transparency is not the primary issue here. Option B is wrong because reliability is about accuracy, not bias.

Option D is wrong because privacy is not violated by using zip code alone.

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194
MCQhard

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

A.Stop word exclusion.
B.Query performance optimization.
C.Tokenization settings.
D.Language bias in the search results.
AnswerD

Bias against certain languages can disadvantage users.

Why this answer

Language bias can lead to unequal search quality for non-English speakers, making it a key ethical concern.

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195
MCQmedium

A company is developing an AI model to screen job applications. The training data is heavily skewed toward candidates from a specific demographic. What is the most important step the team should take to address potential ethical concerns?

A.Remove all sensitive attributes from the data before training.
B.Ensure the training data is diverse and representative of all demographics.
C.Deploy the model quickly and monitor for complaints.
D.Increase the overall size of the training dataset without regard to distribution.
AnswerB

Diverse data reduces bias and improves fairness.

Why this answer

Option B is correct: Using diverse and representative training data helps mitigate bias. Option A is wrong because simply increasing data size without addressing imbalance may amplify bias. Option C is wrong because removing sensitive attributes can still allow proxies.

Option D is wrong because deploying quickly without validation risks ethical harm.

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196
MCQeasy

Refer to the exhibit. An organization implements this AI fairness policy for their Einstein Prediction Builder model. What is the most significant ethical gap in this policy?

A.It does not include enough protected attributes
B.The demographic parity metric is inappropriate for this use case
C.It does not specify a process for reviewing and remediating models that fail the fairness threshold
D.The monitoring schedule is too frequent
AnswerC

Without a remediation process, the policy lacks accountability.

Why this answer

The policy lacks enforcement of human oversight or a process for when the threshold is violated. Option A (It does not specify a process for reviewing models that fail the fairness threshold) is correct because ethical AI requires accountability and remediation. Option B (Protected attributes are too few) - race and gender are key, but not the main gap.

Option C (Monitoring interval) is specified. Option D (Fairness metric choice) - demographic parity is common.

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197
MCQmedium

A non-profit organization uses Salesforce AI to help prioritize grant applications. The AI scores applications based on historical funding decisions, project impact, and community need indicators. After deployment, staff notices that applications from rural areas consistently receive lower scores than those from urban areas, even when project quality is similar. The organization's mission is to serve underserved communities, including rural areas. The AI model is trained on historical data that favored larger, urban projects. The ethics committee is meeting to decide next steps. What is the most appropriate action to align ethical AI with the organization's mission?

A.Retrain the model with new criteria that prioritize rural impact
B.Accept the AI scores but provide staff training on bias
C.Manually overrule the AI scores for rural applicants
D.Continue using the model but add explainability reports
AnswerA

Aligning the model with the mission ensures ethical AI.

Why this answer

The correct answer is C because the model output contradicts the mission, so the model should be retrained with updated criteria that emphasize rural support. Option A is wrong because manually overruling is not scalable and may introduce bias. Option B is wrong because accepting the bias violates the mission.

Option D is wrong because providing explanations does not fix the unfair scoring.

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198
Multi-Selecthard

A Salesforce admin is configuring Einstein Bots. Which TWO actions are essential to maintain ethical AI practices?

Select 2 answers
A.Provide a clear option to escalate to a human agent
B.Log all conversations indefinitely for analysis
C.Disable all feedback loops to avoid data contamination
D.Monitor conversations for biased or offensive responses
E.Let the bot operate without human oversight
AnswersA, D

Human handoff ensures complex issues are handled empathetically.

Why this answer

Options A and C are correct: monitoring for bias (A) and allowing human handoff (C). Option B is wrong because complete autonomy is risky. Option D is wrong because no feedback loop misses quality issues.

Option E is wrong as logging all conversations may violate privacy.

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199
MCQeasy

A sales team uses Einstein Lead Scoring. They notice the model gives low scores to leads from certain industries. The AI Associate suspects bias. What should they do to validate?

A.Run a holdout test to check prediction accuracy.
B.Retrain the model with balanced data.
C.Review the model's confidence intervals.
D.Analyze the distribution of scores across industry segments.
AnswerD

This reveals if certain groups are systematically scored lower.

Why this answer

Option D is correct because analyzing the distribution of scores across industry segments directly validates whether the model exhibits systematic bias. By comparing score distributions, the associate can identify if certain industries are consistently under-scored, which would indicate a biased pattern rather than random variation. This approach aligns with ethical AI practices that require transparency and fairness assessment before any model adjustments.

Exam trap

The trap here is that candidates often confuse model accuracy metrics (like holdout tests) with fairness validation, not realizing that a model can be accurate yet systematically biased against certain subgroups.

How to eliminate wrong answers

Option A is wrong because a holdout test checks prediction accuracy (e.g., AUC, RMSE) but does not reveal how scores are distributed across specific segments like industries; a model can be accurate overall yet still biased against certain groups. Option B is wrong because retraining with balanced data is a corrective action that should only be taken after bias is confirmed; prematurely retraining may mask the underlying issue and waste resources. Option C is wrong because confidence intervals quantify uncertainty around predictions, not the distribution of scores across segments; they do not help detect whether low scores are concentrated in particular industries.

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200
Multi-Selecthard

According to Salesforce's AI ethics principles, which three pillars should guide the development of AI applications?

Select 3 answers
A.Accountability.
B.Transparency.
C.Profitability.
D.Scalability.
E.Fairness.
AnswersA, B, E

Accountability for AI outcomes.

Why this answer

Option A is correct because accountability is one of Salesforce's core AI ethics principles, requiring that organizations take responsibility for the outcomes of their AI systems. This principle ensures that there is human oversight and that AI applications are designed with mechanisms for redress and governance, aligning with Salesforce's commitment to ethical AI development.

Exam trap

Salesforce often tests candidates by including plausible-sounding business or technical terms like 'profitability' or 'scalability' as distractors, leading them to confuse operational goals with ethical mandates.

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201
MCQmedium

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

A.Use only predefined responses
B.Disable the bot for sensitive topics
C.Review training data for representativeness
D.Monitor conversations regularly
AnswerC

Correct. Diverse and representative training data reduces bias.

Why this answer

Reviewing training data for representativeness helps identify and mitigate bias sources.

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202
MCQeasy

Refer to the exhibit. The error log from an AI recommendation system indicates that it cannot explain a decision. Which ethical concern does this directly raise?

A.Bias in recommendations
B.Safety risks
C.Lack of transparency and explainability
D.Lack of human accountability
AnswerC

The error states no explainability, violating transparency.

Why this answer

Option D is correct: Lack of explainability means the system is not transparent. Option A is wrong because bias is not shown. Option B is wrong because safety is not directly indicated.

Option C is wrong because accountability is about oversight, but the issue is lack of explanation.

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203
MCQmedium

An AI system used for medical diagnosis has been shown to have lower accuracy for certain ethnic groups. The development team is considering releasing it anyway because most patients are from the majority group. Which ethical principle is most compromised?

A.Transparency
B.Fairness
C.Accountability
D.Privacy
AnswerB

Unequal performance across groups violates fairness.

Why this answer

The scenario describes an AI system that performs worse for certain ethnic groups, yet the team plans to release it anyway because the majority group is unaffected. This directly violates the principle of fairness, which requires that AI systems do not discriminate or perpetuate bias against any group. Releasing a model with known accuracy disparities without mitigation prioritizes overall performance over equitable treatment, compromising fairness.

Exam trap

Salesforce often tests fairness by presenting a scenario where a model performs well overall but has known disparities for a subgroup, tempting candidates to choose transparency or accountability because they focus on the team's decision to release rather than the core ethical violation of unequal treatment.

How to eliminate wrong answers

Option A is wrong because transparency refers to openness about how the AI system works, its limitations, and its decision-making process; the scenario does not involve hiding information or lack of explainability, but rather knowingly accepting unequal performance. Option C is wrong because accountability concerns who is responsible for the system's outcomes and decisions; while releasing a biased model may raise accountability issues, the core ethical breach here is the unequal treatment itself, not the assignment of responsibility. Option D is wrong because privacy involves the protection of personal data and informed consent; the scenario does not mention any data misuse, unauthorized access, or violation of patient confidentiality.

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204
MCQhard

A company is building an AI model to score sales leads. They have a dataset with historical leads, including whether they converted. The dataset contains 90% male and 10% female leads. The model will be used to prioritize leads for sales follow-ups. What is the primary ethical concern?

A.The training data is imbalanced, which may cause the model to be less accurate for female leads, leading to unfair prioritization.
B.The model will be biased against male leads because they are overrepresented.
C.The dataset is too small to build a reliable model.
D.Using historical data is unethical because it may not reflect current conditions.
AnswerA

Bias in training data leads to biased outcomes.

Why this answer

Option A is correct because the imbalance can lead to the model performing poorly for female leads, causing gender bias. Option B is wrong because the concern is not that accuracy is already biased, but that training data is biased. Option C is wrong because the dataset size is not the main issue; the imbalance is.

Option D is wrong because using historical data is not inherently unethical, but the imbalance is problematic.

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205
MCQmedium

A healthcare provider uses an AI system to predict patient readmission risk. The system was trained on historical data from the past five years, during which the hospital served a predominantly urban population. Recently, the hospital expanded to rural areas with different demographic and socioeconomic profiles. The AI predictions have been less accurate for rural patients, leading to misallocation of care resources. The AI Ethics committee is reviewing the system for potential bias. The model outputs a risk score from 0 to 100. The data science team has identified that the model uses features such as income, distance from hospital, and insurance type, which may correlate with race and socioeconomic status. The team wants to make the model fairer without retraining from scratch. Which approach best balances fairness and predictive accuracy?

A.Remove the features income, distance, and insurance type from the model and retrain.
B.Continue using the current model but add a disclaimer that predictions may be less accurate for rural patients.
C.Apply a post-processing calibration that adjusts the risk score thresholds separately for urban and rural populations to achieve equal false positive rates.
D.Retrain the model using only the latest year of data that includes rural patients.
AnswerC

Post-processing calibration can equalize error rates across groups without retraining, balancing fairness and accuracy.

Why this answer

Option C is correct because post-processing calibration adjusts the decision thresholds for each subgroup (urban vs. rural) to equalize a fairness metric (e.g., false positive rate) without modifying the underlying model. This approach preserves the predictive signal from the original features while directly addressing the bias caused by distribution shift, making it the most practical solution when retraining from scratch is not feasible.

Exam trap

Salesforce often tests the misconception that removing sensitive features (like income or insurance type) is sufficient to eliminate bias, when in reality proxy variables and correlated features can still perpetuate discrimination, making post-processing or reweighing techniques more effective.

How to eliminate wrong answers

Option A is wrong because simply removing correlated features (income, distance, insurance type) does not guarantee fairness—proxy variables or remaining features can still encode the same biases, and the model may lose important predictive signal, reducing accuracy for all groups. Option B is wrong because adding a disclaimer does not mitigate the misallocation of care resources; it merely acknowledges the problem without taking any corrective action, which fails the ethical requirement to actively reduce bias. Option D is wrong because retraining on only the latest year of data would likely produce a model with insufficient sample size for rural patients, leading to high variance and poor generalization, and it ignores the valuable historical data that could still be useful for urban patients.

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206
MCQmedium

A developer notices that an AI model performs differently for different age groups. What should be done?

A.Retrain with more data from all ages.
B.Remove age as a feature.
C.Investigate the cause and evaluate fairness metrics.
D.Ignore it if overall accuracy is high.
AnswerC

This is the proper first step.

Why this answer

Option B is correct because investigating the cause and evaluating fairness metrics is the proper first step. Option A is wrong ignoring can lead to unethical outcomes. Option C is wrong retraining with more data may help but without investigation may not address root cause.

Option D is wrong removing age as a feature may not eliminate bias and could reduce model accuracy.

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207
MCQeasy

Refer to the exhibit. An admin sees this error in the Einstein activity log. What is the most likely cause?

A.Sentiment analysis is generating PII that data masking cannot hide.
B.The model output should not contain any text.
C.The Einstein Trust Layer is completely disabled.
D.Data masking is configured but not applied to the sentiment analysis model.
AnswerD

Since sentiment analysis is off, the PII leak must be from another component without masking.

Why this answer

The error indicates that sentiment analysis is not enabled, but PII is leaking, likely because data masking is not applied to the model generating the output.

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