CCNA Ethical Ai Questions — Page 2 of 2

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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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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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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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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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

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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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

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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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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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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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

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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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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