Courseiva

CCNA Ethical Considerations of AI Questions

75 of 207 questions · Page 1/3 · Ethical Considerations of AI · Answers revealed

Practice these questionsDomain overviewAll questions
1
MCQhard

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

A.Einstein Trust Layer.
B.Permission Set.
C.Einstein Analytics.
D.Data Mask.
AnswerA

Trust Layer provides content moderation and safety features.

Why this answer

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

Exam trap

Salesforce often tests the misconception that any 'Einstein' feature (like Analytics) can handle AI governance, but the Trust Layer is the only dedicated service for auditing and filtering AI outputs for safety and bias.

How to eliminate wrong answers

Option B is wrong because Permission Sets control user access and permissions to objects, fields, and features, not content auditing or AI safety checks. Option C is wrong because Einstein Analytics (now Tableau CRM) is a business intelligence and analytics platform for data visualization and insights, not a tool for auditing AI-generated text for harmful language. Option D is wrong because Data Mask is a security feature that obfuscates sensitive data in non-production environments, not a mechanism to audit or filter AI-generated content for bias or toxicity.

Practice this question →
2
MCQmedium

Refer to the exhibit. A company has the Einstein LLM policy shown. What is the primary ethical gap in this policy?

A.It blocks employment decisions but allows content generation which could be biased
B.It does not include fairness audits for allowed use cases
C.It requires human review for clinical diagnosis but not for legal advice
D.It allows LLM for summarization without requiring human review for high-risk summaries
AnswerD

Summarization could include sensitive content; missing human review is a gap.

Why this answer

Option B is correct because the policy allows using LLM for summarization without specifying oversight for sensitive areas like medical summaries. Option A is wrong because fairness audits are covered. Option C is wrong because content generation is allowed.

Option D is wrong because human review is required for some use cases.

Practice this question →
3
MCQhard

A financial institution uses an AI system to approve loan applications. The system denies loans to applicants from certain postal codes at a higher rate. The model includes 'postal code' as a feature. Which ethical consideration is most directly violated?

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

Disparate impact based on postal code violates fairness principles.

Why this answer

The AI system's use of 'postal code' as a feature leads to disparate impact on applicants from certain areas, directly violating the ethical principle of fairness. Fairness requires that AI models do not discriminate against protected groups or perpetuate systemic biases, even if the feature itself is not a protected attribute. By denying loans at a higher rate based on postal code, the system is likely engaging in proxy discrimination, which is a core fairness violation.

Exam trap

Salesforce often tests the distinction between fairness and transparency, where candidates mistakenly choose transparency because they think the model's use of postal code is 'hidden' or not explainable, but the core violation is the discriminatory outcome, not the lack of explanation.

How to eliminate wrong answers

Option B (Privacy) is wrong because the issue is not about unauthorized access or misuse of personal data, but about biased outcomes from a legitimate feature. Option C (Accountability) is wrong because the question focuses on the ethical violation of the model's behavior, not on who is responsible for its deployment or oversight. Option D (Transparency) is wrong because the problem is not a lack of explainability or interpretability of the model's decisions, but the discriminatory impact of those decisions.

Practice this question →
4
MCQmedium

A company deployed an AI model for lead scoring. After several months, they notice that leads from certain geographic regions consistently receive higher scores than leads from other regions with similar demographic profiles. The company wants to ensure ethical AI usage. What should they do first?

A.Adjust the scoring thresholds for each region to equalize scores.
B.Retrain the model using a more diverse and balanced training dataset.
C.Ignore the discrepancy since the model overall accuracy is high.
D.Remove the geographic region feature from the model completely.
AnswerB

Retraining with diverse data reduces bias by ensuring the model learns from representative examples across all regions.

Why this answer

Option B is correct because retraining with more diverse data addresses potential bias at the source. Option A ignores the issue. Option C adjusts thresholds without fixing root cause.

Option D removes a feature that may be relevant but could still leak bias through correlated features.

Practice this question →
5
MCQhard

A company is deploying an AI system that makes recommendations to users. To ensure ethical use, they should:

A.Allow users to opt out and understand how decisions are made.
B.Make recommendations without oversight.
C.Maximize engagement regardless of user well-being.
D.Use only internal data.
AnswerA

Respects autonomy and transparency.

Why this answer

Option A is correct because allowing users to opt out and understanding decisions respects autonomy and transparency. Option B is wrong maximizing engagement without regard to well-being is unethical. Option C is wrong lack of oversight can lead to harmful outcomes.

Option D is wrong using only internal data may not be sufficient and could raise privacy concerns.

Practice this question →
6
MCQeasy

A company is implementing an AI system to recommend marketing campaigns. To align with Salesforce's ethical AI principles, which practice is most important?

A.Ensure a human reviews AI-generated campaigns before sending
B.Target only high-value customers
C.Maximize open rates by optimizing subject lines
D.Remove human involvement to speed up campaigns
AnswerA

Human review aligns with the principle of human oversight.

Why this answer

The correct answer is D because human oversight ensures that AI recommendations are reviewed for fairness and accuracy. Option A is wrong because maximizing open rates could lead to manipulative practices. Option B is wrong because removing all humans can reduce accountability.

Option C is wrong because targeting only high-value customers may be unfair.

Practice this question →
7
Multi-Selecthard

Which THREE components are essential for an ethical AI governance framework within a large enterprise?

Select 3 answers
A.Establish a cross-functional AI ethics board.
B.Conduct regular ethical impact assessments.
C.Define clear accountability for AI outcomes.
D.Minimize human oversight to reduce operational costs.
E.Optimize for accuracy as the primary goal.
AnswersA, B, C

An ethics board brings diverse perspectives to guide AI development and use.

Why this answer

Options A, B, and C are correct. An ethics board provides oversight, impact assessments identify risks, and accountability ensures responsibility. Minimizing human oversight (D) contradicts governance.

Only focusing on accuracy (E) neglects other ethical dimensions.

Practice this question →
8
MCQmedium

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

A.Invasion of employee privacy.
B.High computational cost.
C.Difficulty in interpreting the model.
D.Overfitting on historical data.
AnswerA

Predicting performance often uses personal data, raising privacy concerns.

Why this answer

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

Exam trap

Salesforce often tests the distinction between ethical concerns (privacy, bias, transparency) and technical issues (cost, performance, overfitting), so the trap here is that candidates may confuse a model's high confidence with accuracy or fairness, overlooking that the ethical problem lies in the unauthorized use of personal data to make high-stakes predictions.

How to eliminate wrong answers

Option B is wrong because high computational cost is a technical or financial concern, not an ethical one; it does not address fairness, privacy, or bias. Option C is wrong because difficulty in interpreting the model (lack of explainability) is a separate ethical issue related to transparency, but the question specifically highlights 'high confidence' predictions, which implies the model is interpretable enough to be confident, so the core ethical concern here is privacy, not interpretability. Option D is wrong because overfitting on historical data is a model performance issue that could lead to inaccurate predictions, but it is not the primary ethical concern when the model is already identifying employees with high confidence; privacy invasion is the direct ethical risk.

Practice this question →
9
MCQeasy

A company uses Einstein Sentiment to analyze customer feedback. The tool incorrectly flags negative sentiment for customers with heavy accents. Which ethical issue is present?

A.Privacy violation
B.Bias and discrimination
C.Accountability gap
D.Lack of transparency
AnswerB

The tool discriminates based on accent, which is a form of bias.

Why this answer

Option A is correct because bias against accents is a fairness issue. Option B is wrong because privacy is about data protection. Option C is wrong as transparency involves explaining decisions.

Option D is wrong because accountability is about responsibility.

Practice this question →
10
Multi-Selecteasy

A company is implementing Salesforce Einstein AI for lead scoring. Which TWO actions align with ethical AI practices?

Select 2 answers
A.Use historical data without review to train the model.
B.Ensure the model uses only non-sensitive personal data.
C.Limit model access to only senior management.
D.Provide clear documentation on how the model makes predictions.
E.Regularly audit the model for biased outcomes.
AnswersD, E

Transparency in model predictions fosters trust and accountability.

Why this answer

Option A is correct because regular audits help detect and mitigate biases. Option C is correct because transparency in model predictions fosters trust and accountability. Option B is wrong because historical data often contains biases that can be amplified.

Option D is wrong because restricting access limits oversight. Option E is wrong because using only non-sensitive data may not be sufficient to address all ethical concerns.

Practice this question →
11
MCQeasy

A company is deploying an AI-powered chatbot for customer service. The chatbot is trained on historical support tickets. Which ethical consideration is MOST important to address before deployment?

A.Minimizing the cost of AI training
B.Ensuring the chatbot responds quickly to all queries
C.Checking for biased or discriminatory patterns in training data
D.Planning for regular model retraining
AnswerC

Bias in training data can lead to unfair or unethical outcomes.

Why this answer

Option C is correct because historical data may contain biased responses, leading to unfair treatment of customers. Option A is wrong because cost is a business consideration, not ethical. Option B is wrong while performance is important, it is secondary to fairness.

Option D is wrong because maintenance is operational.

Practice this question →
12
MCQeasy

An organization wants to implement AI in a way that builds trust. Which practice is most important?

A.Keeping model details secret.
B.Providing explanations for AI decisions.
C.Using complex models for better performance.
D.Using the cheapest data sources.
AnswerB

Increases transparency and trust.

Why this answer

Option B is correct because providing explanations for AI decisions increases transparency and trust. Option A is wrong complexity can reduce trust if not explainable. Option C is wrong secrecy reduces trust.

Option D is wrong cheap data sources may compromise data quality and ethics.

Practice this question →
13
MCQhard

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

A.Contact Salesforce support for a refund.
B.Increase the number of recommendations shown.
C.Disable the recommendation engine immediately.
D.Review the training data for geographic bias.
AnswerD

Data bias is a likely cause and should be examined.

Why this answer

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

Exam trap

Salesforce often tests the misconception that the immediate reaction to AI bias should be to disable the system or escalate to support, rather than following a structured troubleshooting process that starts with data review.

How to eliminate wrong answers

Option A is wrong because requesting a refund does not address the underlying bias issue and is not a technical solution. Option B is wrong because increasing the number of recommendations shown does not fix biased recommendations; it may amplify the unequal access. Option C is wrong because disabling the recommendation engine immediately is a drastic step that may disrupt business operations without first understanding the cause of the bias.

Practice this question →
14
MCQmedium

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

A.Inclusion
B.Accountability
C.Safety
D.Transparency
AnswerD

Employees have a right to know how and why AI monitors them.

Why this answer

Option A is correct because transparency requires informing employees about AI monitoring. Option B is wrong accountability relates to responsibility for actions. Option C is wrong safety relates to harm prevention.

Option D is wrong inclusion relates to diversity and belonging.

Practice this question →
15
MCQeasy

A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?

A.Use only one data source for consistency
B.Maximize the model's accuracy on historical hiring decisions
C.Conduct regular fairness audits on model outcomes
D.Remove all demographic data from the training set
AnswerC

Audits help detect and address disparate impact.

Why this answer

Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.

Exam trap

Salesforce often tests the misconception that removing demographic data (option D) is sufficient to ensure fairness, when in reality proxy variables and model behavior must be actively monitored through audits.

How to eliminate wrong answers

Option A is wrong because using only one data source increases the risk of sampling bias and reduces the model's ability to generalize, potentially amplifying existing disparities rather than ensuring fairness. Option B is wrong because maximizing accuracy on historical hiring decisions can perpetuate and even amplify past biases (e.g., gender or racial discrimination) present in the training data, leading to unfair outcomes. Option D is wrong because simply removing demographic attributes does not eliminate bias; models can still learn proxies (e.g., zip code, name, education) that correlate with protected characteristics, a phenomenon known as 'bias through proxy variables'.

Practice this question →
16
MCQhard

A credit scoring AI uses 50 features including zip code, age, and income. The model has high accuracy but denies credit disproportionately to a protected group. An audit reveals that zip code is a proxy for race. What is the best course of action?

A.Remove zip code from the feature set and retrain.
B.Replace zip code with more relevant non-discriminatory features and retrain with fairness constraints.
C.Keep zip code but add a fairness penalty to the loss function.
D.Increase transparency by publishing the model's decision criteria.
AnswerB

Targeted feature engineering and fairness constraints mitigate bias.

Why this answer

Option B is correct because replacing biased proxy with more relevant features can maintain accuracy while reducing discrimination. Option A is wrong because simply removing zip code may not eliminate all proxies. Option C is wrong because retraining with same data yields same bias.

Option D is wrong because transparency alone doesn't fix bias.

Practice this question →
17
Multi-Selecteasy

Which TWO actions help ensure transparency in AI systems according to Salesforce's ethical AI guidelines?

Select 2 answers
A.Limiting access to model outputs to only a few people.
B.Using complex deep learning models without explanation.
C.Automatically retraining models weekly.
D.Documenting model assumptions and limitations.
E.Providing plain-language explanations of model predictions.
AnswersD, E

Documentation is a transparency best practice.

Why this answer

Option D is correct because documenting model assumptions and limitations is a core transparency practice under Salesforce's ethical AI guidelines. It ensures stakeholders understand the boundaries and potential biases of the AI system, enabling informed trust and accountability.

Exam trap

Salesforce often tests the distinction between operational actions (like retraining) and ethical governance actions (like documentation), leading candidates to mistakenly select technically beneficial but ethically irrelevant options.

Practice this question →
18
MCQmedium

During model development, the data scientist realizes the training data is not representative of the intended population. What should they do?

A.Remove the underrepresented groups from the scope.
B.Increase model regularization.
C.Use the data as is, as the model will generalize.
D.Augment data with synthetic samples for underrepresented groups.
AnswerD

Augmentation helps create a more representative dataset.

Why this answer

Option B is correct because augmenting with synthetic data for underrepresented groups helps create a more representative dataset. Option A is wrong because using non-representative data can lead to biased models. Option C is wrong because removing groups from scope can lead to exclusion.

Option D is wrong because regularization does not address representativeness.

Practice this question →
19
MCQhard

A financial institution deploys an AI model to approve loan applications. The model uses features like income, credit score, and postal code. An audit reveals that the model denies loans at a higher rate for applicants in certain postal codes, which correlate with minority neighborhoods. What should the institution do to align with ethical AI principles?

A.Continue using the model but monitor the denial rates monthly.
B.Add an explanation to applicants in affected areas about why they were denied.
C.Remove postal code from the model and retrain using only non-biased features.
D.Publish a report on the model's disparate impact and accept it as a business risk.
AnswerC

Removing the biased proxy variable helps reduce discrimination.

Why this answer

Option D is correct because removing the biased feature and using alternative features is a direct way to mitigate bias. Option A is wrong as it does not address the root cause. Option B is wrong because it only adds transparency, not fairness.

Option C is wrong because disclosure does not fix the bias.

Practice this question →
20
MCQmedium

A hospital uses an AI triage system to prioritize patients in the emergency department. The AI was trained on historical patient data and assigns priority scores based on vital signs and symptoms. Recently, a study finds that the system consistently assigns lower priority to elderly patients compared to younger patients with similar clinical presentations. The hospital's ethics committee is concerned about age discrimination. The current model achieves high accuracy in predicting outcomes, and doctors have come to rely on it for efficiency. What should the hospital do to address the ethical concern while maintaining clinical effectiveness?

A.Replace the AI triage system with a completely new model built from scratch.
B.Retrain the model with a modified objective that penalizes age-based disparities.
C.Continue using the current model since it has high accuracy and efficiency.
D.Remove age as an input feature from the model.
AnswerB

This approach minimizes bias while retaining the model's predictive power, aligning with fairness and accuracy.

Why this answer

Option A is correct because adjusting the objective function to penalize age bias directly addresses the discrimination while keeping the model effective. Option B removes age, but bias may persist through correlated features. Option C requires building a new model from scratch, which is time-consuming and may not be necessary.

Option D ignores the problem.

Practice this question →
21
Multi-Selectmedium

Which THREE factors should an AI Associate consider when evaluating a model for potential bias?

Select 3 answers
A.The complexity of the model architecture.
B.Whether features are correlated with protected attributes.
C.Disparities in model performance metrics across groups.
D.The date the model was last deployed.
E.Whether the training data is representative of all groups.
AnswersB, C, E

Correlation can lead to proxy discrimination.

Why this answer

Option B is correct because if a feature is correlated with a protected attribute (e.g., race, gender, age), the model may inadvertently learn and perpetuate discriminatory patterns, even if the protected attribute itself is not used as an input. This is a key source of indirect or proxy bias in machine learning systems.

Exam trap

Salesforce often tests the misconception that model complexity or deployment recency are relevant to bias detection, when in fact bias is rooted in data representation and feature correlations with protected attributes.

Practice this question →
22
Multi-Selecthard

Which TWO practices are recommended when using AI for automated decision-making in hiring?

Select 2 answers
A.Use the AI model as the sole decision-maker.
B.Regularly audit the model for adverse impact.
C.Use all available data including protected attributes.
D.Incorporate human review for high-stakes decisions.
E.Ignore adverse impact if the model is accurate.
AnswersB, D

Auditing detects bias.

Why this answer

Option B is correct because regular auditing for adverse impact is a core ethical practice to detect and mitigate bias in AI-driven hiring systems. Audits involve statistical analysis (e.g., the four-fifths rule from the Uniform Guidelines on Employee Selection Procedures) to compare selection rates across protected groups, ensuring the model does not disproportionately disadvantage certain demographics.

Exam trap

Salesforce often tests the misconception that model accuracy alone justifies automated decisions, tempting candidates to pick 'Ignore adverse impact if the model is accurate' (Option E) without recognizing that fairness and ethical compliance are separate, non-negotiable requirements.

Practice this question →
23
Multi-Selectmedium

Which TWO of the following are considered core ethical principles in AI according to Salesforce’s AI Ethics?

Select 2 answers
A.Accountability
B.Popularity
C.Transparency
D.Speed
E.Profitability
AnswersA, C

Accountability means humans are responsible for AI outcomes.

Why this answer

Options B and D are correct: Accountability and Transparency are key principles. Option A (Profitability) is not an ethical principle. Option C (Speed) is not a principle.

Option E (Popularity) is not a principle.

Practice this question →
24
MCQhard

Refer to the exhibit. A team is deploying an AI model for credit scoring. The model uses a complex neural network with high accuracy. The team has performed bias testing and used a representative dataset. According to the policy, what is the MOST significant ethical gap?

A.The training data may not be representative
B.Customer consent was not obtained
C.Bias testing was not performed
D.The model lacks explainability, which is not required by the policy
AnswerD

Explainability is optional but critical for ethical credit scoring.

Why this answer

Option D is correct because the policy explicitly requires explainability for high-risk AI models, such as those used in credit scoring. A complex neural network inherently lacks explainability, and the team has not addressed this requirement, making it the most significant ethical gap despite high accuracy and bias testing.

Exam trap

Salesforce often tests the misconception that high accuracy and bias testing alone satisfy ethical requirements, but in high-risk domains like credit scoring, explainability is a mandatory policy requirement that candidates overlook.

How to eliminate wrong answers

Option A is wrong because the team has already used a representative dataset, so the training data being not representative is not a gap. Option B is wrong because the policy does not explicitly require customer consent for model deployment; the focus is on bias testing, fairness, and explainability. Option C is wrong because bias testing was performed, so this is not a gap.

Practice this question →
25
MCQeasy

A financial services company deploys an AI system to approve small business loans. The system uses a deep neural network trained on historical loan data. After deployment, an internal audit reveals that the approval rate for minority-owned businesses is 15% lower than for non-minority-owned businesses with similar financial profiles. The company's AI Ethics policy requires that AI systems be fair and transparent. The data science team has access to the training data, model architecture, and feature importance scores. The company wants to understand why the disparity exists and take corrective action. Which approach should the team take first?

A.Analyze the training data to determine if there is sampling bias or labeling bias that caused the model to associate minority ownership with higher risk.
B.Apply a disparate impact analysis to quantify the adverse impact and then adjust the decision threshold.
C.Examine the model's weights and activations to identify which features contribute to the disparity.
D.Retrain the model with a fairness constraint that penalizes disparities in approval rates.
AnswerA

Bias often stems from training data; analyzing data for imbalances or incorrect labels is the first logical step.

Why this answer

Option A is correct because the first step in diagnosing an AI fairness issue is to audit the training data for biases such as sampling bias (e.g., underrepresentation of minority-owned businesses) or labeling bias (e.g., historical loan officers unfairly labeling minority applicants as higher risk). Since the team has access to the training data, analyzing it directly addresses the root cause of the disparity before making model-level changes. This aligns with the AI Ethics policy requirement for transparency, as data bias is a common source of unfair outcomes in deep neural networks trained on historical data.

Exam trap

Salesforce often tests the principle that data bias is the most common root cause of AI fairness issues, tempting candidates to jump to model-level fixes (like threshold adjustment or fairness constraints) instead of first auditing the training data for sampling or labeling bias.

How to eliminate wrong answers

Option B is wrong because applying a disparate impact analysis and adjusting the decision threshold treats the symptom (unequal approval rates) rather than investigating the underlying cause in the data or model; it may also violate transparency requirements if the threshold adjustment is not explainable. Option C is wrong because examining model weights and activations in a deep neural network is a black-box approach that is unlikely to reveal clear, interpretable causes of disparity, especially when feature importance scores are already available and the team should first check the data. Option D is wrong because retraining with a fairness constraint is a corrective action that should be taken only after understanding the source of bias; jumping to this step without data analysis risks introducing new biases or masking the original problem.

Practice this question →
26
MCQmedium

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?

A.The model may produce biased outcomes for underserved groups
B.The system failed to log the error
C.The system ignored the drift warning
D.The retraining process is too slow
AnswerA

Lack of data for a group can lead to biased predictions.

Why this answer

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.

Exam trap

Salesforce often tests the distinction between ethical concerns and operational or technical issues, so candidates may mistakenly choose a performance-related option (like retraining being too slow) instead of recognizing the fairness and bias implications of a model failing to adapt to data drift for a specific population.

How to eliminate wrong answers

Option B is wrong because the error log explicitly shows a drift warning and a retraining failure, meaning the system did log the error. Option C is wrong because the system did not ignore the drift warning; it attempted retraining but failed due to insufficient data. Option D is wrong because the retraining process being too slow is a performance issue, not the primary ethical concern; the core ethical issue is the potential for biased outcomes when retraining cannot occur.

Practice this question →
27
MCQeasy

A company launches a chatbot that interacts with customers. The chatbot does not disclose that it is an AI. Which ethical principle is most directly violated?

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

Users have a right to know they are interacting with AI.

Why this answer

Option B is correct: Transparency requires disclosing that the interaction is with AI. Option A is wrong because accountability is about responsibility. Option C is wrong because fairness is about bias.

Option D is wrong because privacy is about data handling.

Practice this question →
28
MCQeasy

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

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

Customer financial information is sensitive and must be protected.

Why this answer

Revealing personal financial data violates the principle of privacy.

Practice this question →
29
MCQmedium

A nonprofit uses Salesforce AI to prioritize outreach to donors. The model recommends contacting only high-income individuals. Which ethical principle is most compromised?

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

Excluding low-income donors is unfair and contrary to nonprofit mission.

Why this answer

Option C is correct because fairness requires equal treatment and not excluding based on income. Option A is wrong as privacy is not directly violated. Option B is wrong because the model may be transparent but still unfair.

Option D is wrong because accountability is secondary.

Practice this question →
30
MCQhard

A financial institution uses Einstein Discovery to analyze loan applications. The model denies loans at a higher rate for a particular ethnicity. The data is unbiased, but the model learned societal biases. Which action BEST aligns with ethical AI practices?

A.Apply fairness constraints and re-evaluate the model
B.Provide an explanation to denied applicants
C.Use the model as is since the data is unbiased
D.Rely on standard performance metrics like accuracy
AnswerA

Fairness constraints help reduce bias and promote equitable outcomes.

Why this answer

Option D is correct because using fairness metrics and adjusting thresholds can mitigate learned bias. Option A is wrong as it may still discriminate. Option B is wrong because explanation alone doesn't fix bias.

Option C is wrong as standard metrics may not capture fairness.

Practice this question →
31
MCQeasy

A Salesforce admin is setting up an AI-powered lead scoring system. To ensure ethical use, what should they prioritize?

A.Ensure the training data includes as many records as possible
B.Set the system to automatically prioritize the highest-scoring leads
C.Regularly audit the model for bias and adjust scoring to ensure fairness across customer segments
D.Use the default model provided by Salesforce without customization
AnswerC

Ongoing audits and adjustments uphold ethical standards.

Why this answer

Ethical AI requires fairness, accountability, and transparency. Option C (regularly auditing for bias and ensuring fair treatment across segments) is the most comprehensive approach. Option A (prioritizing highest scoring leads) may ignore bias.

Option B (using default model) may inherit biases. Option D (focusing only on data volume) does not address ethical concerns.

Practice this question →
32
MCQeasy

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

A.Avoid using any images that contain people.
B.Test the model for bias across different demographic groups.
C.Use only high-resolution images.
D.Only use images from a single demographic.
AnswerB

Directly addresses fairness.

Why this answer

Testing for bias across demographic groups helps ensure the model treats all users fairly.

Practice this question →
33
MCQeasy

A data scientist is training a model to predict customer churn. To ensure fairness, what should the data scientist do?

A.Focus solely on model accuracy ignoring demographic groups.
B.Ensure the training data is representative of the entire customer base.
C.Remove all demographic attributes from the dataset.
D.Use only historical data without checking for bias.
AnswerB

Representative data reduces the risk of bias.

Why this answer

Option B is correct because ensuring the training data is representative of the entire customer base directly addresses fairness by preventing underrepresentation or overrepresentation of specific demographic groups. A representative dataset helps the model learn unbiased patterns across all segments, reducing the risk of disparate impact. This aligns with the principle of fairness in AI, where the model's predictions should not systematically disadvantage any group.

Exam trap

Salesforce often tests the misconception that simply removing sensitive attributes (like race or gender) is sufficient to ensure fairness, when in reality the model can still learn proxies for those attributes from other correlated features.

How to eliminate wrong answers

Option A is wrong because focusing solely on model accuracy while ignoring demographic groups can lead to a model that performs well overall but has high error rates for minority groups, violating fairness principles. Option C is wrong because simply removing all demographic attributes does not guarantee fairness; the model can still learn proxies for those attributes from other correlated features (e.g., zip code for race), a phenomenon known as 'redundant encoding.' Option D is wrong because using only historical data without checking for bias propagates existing societal biases present in the data, leading to discriminatory outcomes.

Practice this question →
34
MCQmedium

A company uses Salesforce Einstein to build an AI model that predicts customer churn. The model is trained on historical data from the past two years. During testing, the model shows significantly higher accuracy for male customers compared to female customers. What is the most ethical course of action?

A.Deploy the model but add a disclaimer that it may be less accurate for female customers.
B.Deploy the model as is, because it still meets the overall accuracy threshold.
C.Investigate the cause of the disparity, retrain the model with more representative data, and re-evaluate fairness.
D.Manually adjust the model's output to ensure equal churn predictions across genders.
AnswerC

This addresses bias and upholds fairness principles.

Why this answer

Option B is correct because investigating and retraining the model to reduce bias is the ethical approach. Option A is wrong because ignoring the disparity could lead to unfair treatment. Option C is wrong because immediately deploying without correction is irresponsible.

Option D is wrong because manually adjusting predictions introduces new biases.

Practice this question →
35
MCQhard

An AI system for hiring is found to have a disparate impact on a protected class. The company is legally required to...

A.Conduct a bias audit and take corrective action.
B.Obtain consent from applicants.
C.Publish the algorithm.
D.Discontinue use of the system.
AnswerA

This is typically legally required.

Why this answer

Option B is correct because under many regulations, disparate impact triggers a requirement for bias audit and corrective action. Option A is wrong because discontinuation is not immediately required; mitigation is possible. Option C is wrong because obtaining consent does not address the impact.

Option D is wrong because publishing the algorithm is not typically a legal requirement.

Practice this question →
36
Multi-Selectmedium

Which THREE are key principles of Salesforce's AI Ethics framework? (Choose three.)

Select 3 answers
A.Be Inclusive
B.Be Fast Over Accurate
C.Be Accountable
D.Be Transparent
E.Be Profit-Maximizing
AnswersA, C, D

Inclusivity ensures fairness for all users.

Why this answer

Options A, C, and D are correct. Option B is wrong because 'maximum profit' is not an ethical principle. Option E is wrong because 'speed over accuracy' contradicts responsible AI.

Practice this question →
37
MCQhard

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

A.Increase model regularization
B.Remove the gender feature
C.Reduce model complexity
D.Use adversarial debiasing technique
AnswerD

Adversarial debiasing explicitly reduces bias in learned representations.

Why this answer

Option B is correct because adversarial debiasing directly reduces bias in model representations. Option A is wrong because removing the gender feature may not eliminate bias if other features correlate. Option C is wrong because regularization helps control overfitting, not bias.

Option D is wrong because reducing complexity may not address bias.

Practice this question →
38
MCQhard

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

A.Underfitting
B.Data imbalance
C.Lack of compute resources
D.Overfitting
AnswerB

Data imbalance causes the model to perform poorly on underrepresented groups, such as minority language queries.

Why this answer

Option C is correct because data imbalance means the training data had few examples of minority language queries, leading to poor performance. Option A is wrong because overfitting would cause poor generalization on all new data, not specifically minority languages. Option B is wrong because underfitting would cause poor performance on all data.

Option D is wrong because compute power does not cause this specific bias.

Practice this question →
39
MCQeasy

A company is developing a chatbot for customer service. They want to ensure the bot does not generate offensive responses. Which practice should they implement?

A.Use unsupervised learning to allow the bot to learn naturally from conversations.
B.Remove all toxicity detection to avoid false positives.
C.Train the model only on customer service transcripts without review.
D.Deploy a content filter and human-in-the-loop moderation for sensitive interactions.
AnswerD

Content filters and human oversight prevent harmful outputs.

Why this answer

Option D is correct because deploying a content filter combined with human-in-the-loop moderation provides both automated detection of offensive language and human oversight for ambiguous or sensitive interactions. This layered approach ensures that the chatbot can block clearly toxic outputs in real time while allowing human reviewers to handle edge cases, reducing the risk of generating offensive responses.

Exam trap

Salesforce often tests the misconception that unsupervised learning or removing safeguards is acceptable for safety, when in fact explicit content filtering and human oversight are required to prevent offensive outputs.

How to eliminate wrong answers

Option A is wrong because unsupervised learning on raw conversations would expose the model to toxic language without guidance, likely causing it to learn and reproduce offensive patterns. Option B is wrong because removing toxicity detection eliminates the primary safeguard against offensive outputs, directly contradicting the goal of preventing harmful responses. Option C is wrong because training solely on customer service transcripts without review can still include subtle biases or inappropriate language, and the model may overfit to narrow patterns without generalizing safely.

Practice this question →
40
Multi-Selectmedium

A healthcare provider uses AI to predict patient outcomes. Which THREE measures should be implemented to ensure ethical AI use?

Select 3 answers
A.Establish a human-in-the-loop for critical decisions
B.Implement strict data anonymization and consent protocols
C.Use a black-box model for better accuracy
D.Optimize model for maximum profit
E.Regularly test for bias across demographic groups
AnswersA, B, E

Human oversight ensures accountability and safety.

Why this answer

Options A, C, and D are correct: human oversight (A), data privacy (C), and fairness (D). Option B is wrong because black-box models lack transparency. Option E is wrong as profit maximization is not ethical goal.

Practice this question →
41
MCQmedium

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

A.Use only demographic features
B.Remove all user data
C.Regular bias audits
D.Increase model depth
AnswerC

Bias audits proactively identify and address unfair patterns in recommendations.

Why this answer

Option B is correct because regular bias audits help detect and mitigate unfairness over time. Option A is wrong because using only demographic features could lead to stereotype-based recommendations. Option C is wrong because removing all user data would eliminate personalization.

Option D is wrong because increasing model depth does not address fairness.

Practice this question →
42
MCQeasy

A company uses an AI model to screen job applications. They discover the model is less likely to recommend female candidates. What should the company prioritize first?

A.Remove gender-related features from the model.
B.Re-train the model with only male candidates.
C.Implement a fairness metric to evaluate bias.
D.Increase the model's training data size.
AnswerC

Fairness metrics help quantify bias, which is the first step toward mitigation.

Why this answer

Option D is correct because the first step is to measure the extent of bias using fairness metrics before taking corrective action. Option A is wrong because training only on males would worsen bias. Option B is wrong because removing features may not eliminate proxy bias.

Option C is wrong because simply increasing data may not address the underlying bias.

Practice this question →
43
MCQmedium

Refer to the exhibit. What does the "Status: FAIL" indicate?

A.The model's overall accuracy is low.
B.The data is missing age_group information.
C.The model is not allowed for deployment.
D.The model shows a significant disparity in true positive rates across age groups.
AnswerD

Equal opportunity difference measures TPR disparity.

Why this answer

Option B is correct because equal opportunity difference measures disparity in true positive rates across groups; a result exceeding threshold indicates significant disparity. Option A is wrong the exhibit doesn't state deployment restriction. Option C is wrong overall accuracy is not measured here.

Option D is wrong the data includes age_group.

Practice this question →
44
Multi-Selectmedium

Which THREE factors should be considered when evaluating the fairness of an AI model?

Select 3 answers
A.Disparate impact ratio across groups.
B.Overall accuracy on the test set.
C.Model training time.
D.Equal opportunity difference.
E.Demographic parity in predictions.
AnswersA, D, E

Measures adverse impact ratio.

Why this answer

Option A is correct because the disparate impact ratio measures whether an AI model's predictions disproportionately harm or benefit certain demographic groups, typically by comparing selection rates across groups. A ratio below 0.8 or above 1.25 is often considered evidence of adverse impact, making it a key quantitative fairness metric.

Exam trap

Salesforce often tests the distinction between performance metrics (like accuracy) and fairness metrics, trapping candidates who assume a high-accuracy model is automatically fair.

Practice this question →
45
MCQmedium

An AI system recommends job candidates to recruiters. The system was trained on resumes of past successful hires, most of whom were male. As a result, it consistently ranks female candidates lower. What is the most appropriate mitigation?

A.Re-sample the training data to include more female candidates and use fairness-aware algorithms.
B.Add a post-processing adjustment to increase female candidates' scores.
C.Accept the bias as a reflection of historical data.
D.Remove the gender feature from the model.
AnswerA

Balancing data and using fairness techniques reduce bias.

Why this answer

Option C is correct because ensuring gender balance in training data addresses the root cause. Option A is wrong because removing gender may not eliminate proxy variables like 'years of experience gaps.' Option B is wrong because ignoring the issue perpetuates bias. Option D is wrong because post-processing adjustments may not be sufficient without data changes.

Practice this question →
46
MCQhard

A large e-commerce company uses Salesforce Einstein to recommend products to customers. The AI model is trained on purchase history, browsing behavior, and demographic data including age and gender. Recently, the company received complaints that the model seems to recommend lower-priced items to female customers and higher-priced items to male customers for the same product categories. The data science team confirms the model has a statistically significant difference in recommendation value by gender. The company's ethical AI policy requires fairness, transparency, and human oversight. The compliance team is considering several actions. Which action should the company take first?

A.Adjust the model to increase recommendation prices for female customers
B.Disable the recommendation system until the issue is resolved
C.Conduct a thorough bias audit to identify all sources of disparate impact
D.Immediately remove gender from the training data and retrain the model
AnswerC

A bias audit is essential to understand the full extent and root causes before taking action.

Why this answer

The correct answer is B because conducting a bias audit is the ethical first step to understand the root cause and scope of the bias before taking corrective action. Option A is wrong because removing gender from the model may not eliminate proxy effects. Option C is wrong because disabling the model without analysis loses business value and does not address how to fix it.

Option D is wrong because increasing prices for females would introduce a new bias and is unethical.

Practice this question →
47
MCQhard

A large enterprise uses multiple Salesforce AI services including Einstein Bots, Prediction Builder, and Next Best Action. They want to create a consistent ethical AI policy across all services. Which action is most effective?

A.Limit AI usage to only one Salesforce AI service to simplify oversight
B.Allow each business unit to define its own ethical guidelines for its respective AI service
C.Develop a centralized AI ethics framework that applies to all Salesforce AI services and train staff on its principles
D.Rely on existing legal and regulatory compliance requirements to guide ethical use
AnswerC

A unified framework ensures consistent ethical practices.

Why this answer

Option C (Develop a centralized AI ethics framework that applies to all Salesforce AI services and train staff on its principles) is correct because consistency requires a unified framework. Option A (letting each team set their own policies) leads to inconsistency. Option B (using only one AI service) is impractical.

Option D (relying on legal compliance alone) may not cover all ethical aspects.

Practice this question →
48
MCQmedium

A healthcare organization uses AI to prioritize patient appointments. The AI gives lower priority to patients with a specific chronic condition. To ensure ethical AI, what should the organization do?

A.Train the model on more data from patients with that condition
B.Immediately disable the AI system and revert to manual scheduling
C.Accept the model's decisions since they are based on data
D.Conduct a fairness audit and involve medical experts to review the model's decisions
AnswerD

Combining technical and domain expertise ensures ethical oversight.

Why this answer

Option B (Conduct a fairness audit and involve medical experts to review the model's decisions) is correct because it combines technical assessment with domain expertise. Option A (immediately disable the AI) may be too drastic. Option C (accept the model as is) ignores the bias.

Option D (train the model on more data of that condition) might not correct the prioritization logic.

Practice this question →
49
MCQmedium

A company deploys an Einstein AI model that recommends products to customers. To ensure transparency, what should the company include in the customer-facing interface?

A.A statement that each recommendation is personally selected by a human expert.
B.A simple label 'AI-generated' without further explanation.
C.A complete list of all features used by the model.
D.A brief note that recommendations are generated by AI, along with a way to get more information about why a product was suggested.
AnswerD

Transparency means users know AI is involved and can inquire further.

Why this answer

Option B is correct because informing customers that AI is used and explaining how recommendations are generated aligns with transparency. Option A is wrong as it gives too much detail. Option C is wrong because not all recommendations need to be explained per se, but the system's behavior should be transparent.

Option D is wrong because a simple 'AI-generated' note is insufficient.

Practice this question →
50
MCQmedium

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

A.Increase model complexity
B.Add more features to the model
C.Remove zip code feature
D.Audit training data for bias
AnswerD

Auditing data helps identify biased patterns and is the recommended first step.

Why this answer

Option B is correct because auditing the training data for bias is the first step to identify and mitigate unfairness. Option A is wrong because increasing model complexity may exacerbate bias. Option C is wrong because removing the zip code feature alone does not address underlying bias in other correlated features.

Option D is wrong because adding more features without bias analysis could introduce more bias.

Practice this question →
51
MCQmedium

An AI system is used to approve loan applications. The model uses income, zip code, and credit score as features. What is a potential ethical concern?

A.Zip code may act as a proxy for race, leading to discrimination
B.The model should be a black box to avoid bias
C.Credit scores are rarely accurate
D.Income is not a reliable predictor of repayment
AnswerA

Using zip code can indirectly discriminate based on race or ethnicity.

Why this answer

Option A is correct because using zip code as a feature can introduce proxy discrimination. Zip codes are strongly correlated with race and socioeconomic status due to historical redlining and residential segregation. When the model learns patterns from zip code, it may inadvertently deny loans to applicants from certain racial or ethnic groups, violating fair lending laws and ethical AI principles.

Exam trap

Salesforce often tests the misconception that bias is only introduced by explicitly using protected attributes, when in fact proxy features like zip code can cause discrimination even if race or gender is not directly used.

How to eliminate wrong answers

Option B is wrong because making the model a black box does not avoid bias; in fact, black-box models obscure how decisions are made, making it harder to detect and mitigate bias. Option C is wrong because credit scores, while not perfect, are statistically validated predictors of repayment behavior and are widely used in the financial industry; the ethical concern is not about their accuracy but about how they are combined with other features. Option D is wrong because income is a strong predictor of repayment ability; the ethical issue is not its reliability but the potential for discrimination when combined with proxy features like zip code.

Practice this question →
52
MCQeasy

A company wants to use Einstein OCR to extract text from uploaded documents. To protect customer privacy, what should they ensure before processing documents containing personal data?

A.Ask customers to manually blur sensitive information before uploading.
B.Use HTTPS to securely upload documents.
C.Obtain consent from customers and rely on the model to ignore sensitive data.
D.Enable the Einstein Trust Layer to mask sensitive data and comply with data protection policies.
AnswerD

The Trust Layer is designed for privacy and security.

Why this answer

Option A is correct because the Einstein Trust Layer provides data masking and privacy controls. Option B is wrong because anonymization by users is unreliable. Option C is wrong as encryption protects data in transit but does not address data use.

Option D is wrong because consent alone does not protect privacy if data is exposed.

Practice this question →
53
MCQhard

A hospital uses an AI model to predict patient deterioration. The model was trained on data from a single hospital with a predominantly white patient population. When deployed at a hospital serving a diverse population, the model underperforms for minority groups. What is the most effective way to address this ethical issue?

A.Create separate models for each demographic group to ensure accuracy.
B.Continuously monitor model performance across demographic groups and report disparities.
C.Retrain the model using a more diverse dataset that represents the target population.
D.Adjust the decision threshold for minority groups to improve sensitivity.
AnswerC

Diverse training data improves fairness and performance.

Why this answer

Option D is correct because retraining with diverse data from the target population addresses the root cause. Option A is wrong as it only monitors without improvement. Option B is wrong because adjusting thresholds may not fix the underlying model bias.

Option C is wrong because creating separate models for each group could be logistically complex and stigmatizing.

Practice this question →
54
Multi-Selecthard

Which THREE strategies can help mitigate bias in an AI model? (Choose three.)

Select 3 answers
A.Remove protected attributes from training data
B.Focus training on majority group data for accuracy
C.Randomize a portion of model outputs
D.Use diverse and representative training data
E.Apply fairness metrics during model evaluation
AnswersA, D, E

Removing attributes like race/gender can prevent direct discrimination.

Why this answer

Option A is correct because removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.

Exam trap

Salesforce often tests the misconception that simply removing protected attributes or randomizing outputs is sufficient to eliminate bias, when in fact bias can persist through proxies and requires comprehensive fairness evaluation and diverse data.

Practice this question →
55
Multi-Selecthard

Which TWO are best practices for mitigating bias in AI models?

Select 2 answers
A.Using complex deep learning models.
B.Using balanced training datasets.
C.Removing all sensitive attributes.
D.Training models on the most recent data only.
E.Applying disparity analysis.
AnswersB, E

Balanced datasets help reduce bias from imbalanced representation.

Why this answer

Options A and C are correct. Using balanced training datasets helps reduce representation bias, and applying disparity analysis identifies where bias exists. Option B is wrong because removing sensitive attributes alone may not eliminate proxy variables.

Option D is wrong because using only recent data may still contain bias. Option E is wrong because complex deep learning models can be harder to audit and may amplify bias.

Practice this question →
56
Multi-Selectmedium

Which THREE are key ethical considerations for AI according to Salesforce?

Select 3 answers
A.Accountability
B.Profitability
C.Transparency
D.Privacy
E.Speed
AnswersA, C, D

Organizations must take responsibility for AI outcomes.

Why this answer

Options A, C, and D are correct. Privacy, transparency, and accountability are foundational ethical principles for AI. Option B is wrong because profitability is a business goal, not an ethical consideration.

Option E is wrong because speed is a performance attribute.

Practice this question →
57
MCQeasy

A retail company wants to use Einstein AI to personalize marketing offers. They plan to include customer purchase history and demographic data. What is the essential first step to ensure ethical use of customer data?

A.Only use data from customers who have not opted out of marketing.
B.Obtain explicit consent from customers for using their data in AI personalization.
C.Proceed with available data since it was collected for business purposes.
D.Anonymize all customer data before feeding it into the model.
AnswerB

Consent is foundational for ethical data use.

Why this answer

Option A is correct because obtaining explicit consent aligns with privacy ethics and regulations like GDPR. Option B is wrong as anonymization alone does not replace consent. Option C is wrong because it bypasses transparency.

Option D is wrong because consent is required even if data is already collected.

Practice this question →
58
Multi-Selecthard

Which THREE of the following are effective strategies to mitigate bias in AI models?

Select 3 answers
A.Ensuring training data is diverse and representative
B.Conducting regular bias audits on model outcomes
C.Reducing the complexity of the model
D.Using only historical data without modification
E.Involving a diverse team in model development
AnswersA, B, E

Diverse data reduces the risk of bias.

Why this answer

Options B, C, and E are correct: Diverse training data, regular bias audits, and inclusive design teams help mitigate bias. Option A (Using only historical data) can perpetuate bias. Option D (Reducing model complexity) may not address bias directly.

Practice this question →
59
MCQhard

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?

A.Implement a fairness constraint in the model's loss function to penalize disparities across language groups.
B.Conduct a fairness audit using diverse test cases from multiple languages and dialects to quantify the disparity.
C.Disable the chatbot for non-English languages and redirect those customers to human agents.
D.Collect more training data from all regions and retrain the model from scratch.
AnswerB

An audit with diverse test cases will identify the specific gaps, allowing targeted and cost-effective improvements.

Why this answer

Option B is correct because 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.

Exam trap

Salesforce often tests the principle that measurement and diagnosis must precede intervention; the trap here is that candidates may jump to a technical fix (like a fairness constraint) or a drastic operational change (like disabling the chatbot) without first conducting the essential diagnostic step of a fairness audit.

How to eliminate wrong answers

Option A is wrong because implementing a fairness constraint in the loss function is a technical intervention that should only be applied after the specific disparities have been identified and understood; applying it blindly can degrade overall model performance or introduce new biases without addressing the root cause. Option C is wrong because disabling the chatbot for non-English languages is a reactive, non-technical workaround that reduces service availability and customer satisfaction, failing to leverage the AI's potential and contradicting the goal of maintaining overall performance. Option D is wrong because collecting more training data from all regions and retraining from scratch is a costly, time-consuming approach that should be guided by the results of a fairness audit; without first quantifying the disparity, the new data may not address the specific failure modes, and the retraining may not be necessary if the issue can be fixed with targeted fine-tuning or data augmentation.

Practice this question →
60
MCQhard

Refer to the exhibit. Which ethical principle is most at risk with this AI model configuration?

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

Explainability is disabled, so the model acts as a black box.

Why this answer

The configuration has explainability disabled, meaning the model's decisions cannot be interpreted. This violates transparency. Option B (Transparency) is correct.

Option A (Fairness) is partially addressed by the fairness check, though not fully. Option C (Accountability) is at risk because human review is not required, but the most direct risk is transparency. Option D (Privacy) is not directly affected.

Practice this question →
61
MCQmedium

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

A.Reduce the cost of agent training.
B.Maximize the number of recommendations.
C.Increase the speed of case resolution.
D.Ensure customer data is anonymized.
AnswerD

Anonymization protects privacy and reduces risk of PII exposure in AI outputs.

Why this answer

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

Practice this question →
62
Multi-Selecthard

A company is developing an AI system to assist with hiring. Which TWO practices are essential for ethical AI deployment?

Select 2 answers
A.Optimize the system for speed to reduce waiting times
B.Remove all demographic features to ensure fairness
C.Conduct regular bias audits on model predictions
D.Maximize accuracy on historical hiring data
E.Obtain informed consent from applicants if their data is used
AnswersC, E

Audits help detect and mitigate discriminatory outcomes.

Why this answer

Option C is correct because regular bias audits are a core ethical practice for AI systems, especially in hiring. These audits involve systematically testing the model's predictions across demographic groups to detect and mitigate unintended discrimination, ensuring compliance with fairness standards like the EEOC's Uniform Guidelines on Employee Selection Procedures.

Exam trap

Salesforce often tests the misconception that removing demographic features (Option B) is sufficient to eliminate bias, when in fact proxy variables and model behavior must be actively monitored through audits (Option C).

Practice this question →
63
MCQhard

A large financial institution uses Einstein Discovery to automate loan pre-approval decisions. The model was trained on ten years of historical data. After deployment, the compliance team finds that the approval rate for minority groups is 15% lower than the majority group, even after controlling for credit score and income. The data is balanced across groups. The model uses features like zip code, employment history, and debt-to-income ratio. The institution has a strict policy of fairness and non-discrimination. The AI team proposes three options: (1) remove zip code and employment history from the model, (2) add a fairness constraint to the model training, (3) lower the decision threshold for minority groups to balance approval rates. The compliance officer must choose the most ethical and effective course of action that aligns with Salesforce AI ethical guidelines. Which option should they choose?

A.Add a fairness constraint to the model training
B.Lower the decision threshold for minority groups
C.Remove zip code and employment history from the model
D.Continue using the model as is, since data is balanced
AnswerA

Fairness constraints adjust the model to reduce bias while maintaining accuracy.

Why this answer

Option B is correct because adding a fairness constraint directly addresses bias without arbitrary threshold changes (Option C) and while removing features (Option A) may not eliminate bias due to correlated features. Option A is wrong because zip code and employment history may be proxies; removing them could reduce predictive power without fully solving bias. Option C is wrong because it applies different standards to groups, which may be discriminatory and illegal.

Option D is to continue using the model, which is unethical.

Practice this question →
64
Multi-Selecteasy

Which TWO are key principles of Salesforce's AI ethics? (Choose two.)

Select 2 answers
A.Speed of deployment
B.Profitability
C.Transparency
D.Accountability
E.Full automation
AnswersC, D

Salesforce emphasizes explainable AI.

Why this answer

Options A and C are correct because transparency and accountability are core principles. Option B is wrong because profitability is not an ethical principle. Option D is wrong because automation is a capability, not a principle.

Option E is wrong because speed is not an ethical principle.

Practice this question →
65
MCQhard

A company's Einstein Discovery model for customer lifetime value shows a significant correlation between predicted value and customer's postal code. The company is concerned about ethical implications. What is the most appropriate response?

A.Remove the postal code field from the model immediately
B.Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints
C.Add more demographic data to the model to improve its accuracy
D.Ignore the correlation since the model is predicting business value, not demographic attributes
AnswerB

This approach addresses the ethical concern while preserving model utility.

Why this answer

Option B (Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints) is correct because postal code can be a proxy for race or income. Option A (removing postal code outright) may not be straightforward. Option C (ignoring correlation as coincidental) is unethical.

Option D (adding more demographic data) could increase bias.

Practice this question →
66
Multi-Selectmedium

An AI system is used to detect fraud in financial transactions. Which THREE steps should be taken to address ethical concerns?

Select 3 answers
A.Lower the fraud detection threshold to catch more cases
B.Automatically accept all flagged transactions to improve user experience
C.Implement a human-in-the-loop for high-stakes decisions
D.Ensure the model provides explanations for its decisions
E.Test the model for disparate impact across demographic groups
AnswersC, D, E

Human oversight ensures accountability.

Why this answer

Option C is correct because implementing a human-in-the-loop ensures that high-stakes decisions, such as blocking a legitimate transaction or allowing a potentially fraudulent one, are reviewed by a human before final action. This addresses ethical concerns by preventing fully automated decisions that could cause financial harm or violate user trust, and it aligns with principles of accountability and fairness in AI governance.

Exam trap

Salesforce often tests the misconception that ethical AI is solely about improving model performance or user experience, when in fact it requires balancing accuracy, fairness, and human oversight—candidates may incorrectly choose options that sound beneficial (like lowering thresholds) without considering the ethical trade-offs.

Practice this question →
67
MCQmedium

A retail company uses Salesforce Einstein Vision to analyze customer images for product recommendations. The AI team notices that the model performs poorly on images of customers with darker skin tones, leading to fewer recommendations for that demographic. The team has access to a dataset of diverse skin tones but the company's data privacy policy prohibits using demographic data in training. What should the team do?

A.Retrain the model on the diverse dataset without considering privacy regulations.
B.Ignore the performance disparity as it is a result of natural data distribution.
C.Document the bias, escalate to the ethics board, and seek guidance on using diverse data while maintaining privacy.
D.Use the diverse dataset but remove skin tone labels to avoid privacy issues.
AnswerC

This addresses the bias through proper channels while respecting privacy policies.

Why this answer

Option D is correct because it addresses the bias through proper channels while respecting privacy policies. Option A is wrong because removing labels may still encode bias and could violate the spirit of privacy. Option B is wrong because ignoring performance disparities is unethical.

Option C is wrong because violating privacy regulations is not acceptable.

Practice this question →
68
Multi-Selectmedium

Which TWO actions are most effective in promoting transparency in AI systems? (Choose two.)

Select 2 answers
A.Publish the entire source code of the model online.
B.Provide a model card that describes the purpose, accuracy, and limitations of the AI model.
C.Withhold the data sources used for training to protect the company's competitive advantage.
D.Encrypt all customer data used in the model.
E.Offer an explanation feature that shows why a specific prediction was made for a given user.
AnswersB, E

Model cards are a standard transparency tool.

Why this answer

Options B and C are correct. Option A is wrong because hiding the data sources reduces transparency. Option D is wrong as it relates to privacy, not transparency.

Option E is wrong because publishing details without context can be confusing.

Practice this question →
69
Multi-Selectmedium

Which TWO actions are most effective for ensuring fairness in an AI model used for loan approvals?

Select 2 answers
A.Regularly audit model outcomes for disparate impact across demographic groups.
B.Rely solely on historical data without any adjustments.
C.Allow the model to self-correct over time without human intervention.
D.Use diverse and representative training data.
E.Use a single, simple algorithm to avoid complexity.
AnswersA, D

Audits identify bias even after deployment, enabling corrective action.

Why this answer

Options A and B are correct. Using diverse training data helps the model learn from all groups, and regular auditing detects bias. Option C limits complexity.

Option D perpetuates historical bias. Option E is not a systematic approach.

Practice this question →
70
MCQeasy

A company wants to use customer data to train an AI model. Which ethical consideration is paramount?

A.Model accuracy
B.Cost efficiency
C.Data minimization
D.Speed of deployment
AnswerC

Ensures only necessary data is collected, reducing privacy risks.

Why this answer

Option B is correct because data minimization ensures only necessary data is collected, reducing privacy risks. Option A is wrong while important, accuracy is secondary to ethical data use. Option C is wrong cost efficiency is not an ethical consideration.

Option D is wrong speed of deployment is not an ethical consideration.

Practice this question →
71
MCQmedium

Refer to the exhibit. A Salesforce admin is reviewing an AI model's fairness report. Which action should the admin take?

A.Remove the email_engagement feature to improve fairness.
B.Retrain the model because the equal opportunity score is below threshold.
C.Increase the bias threshold to 0.9.
D.Deploy the model because all metrics exceed the threshold.
AnswerB

The low equal opportunity score indicates bias that needs mitigation.

Why this answer

Option B is correct because the equal opportunity score (0.72) is below the bias threshold (0.8), indicating potential unfairness in true positive rates across groups. Option A is wrong because demographic parity is above threshold but equal opportunity is not, so not all metrics exceed threshold. Option C is wrong because removing features may not address the root cause.

Option D is wrong because increasing the threshold would mask the problem.

Practice this question →
72
MCQmedium

Refer to the exhibit. Based on the JSON policy for AI fairness checks, which fairness metric is NOT enabled?

A.Demographic parity
B.All are enabled
C.Disparate impact
D.Equal opportunity
AnswerD

Correct. The 'equal_opportunity' field is false.

Why this answer

Option D (Equal opportunity) is correct because the JSON policy shown in the exhibit configures fairness checks for demographic parity, disparate impact, and equalized odds, but does NOT include the equal opportunity metric. Equal opportunity requires equal true positive rates across groups, which is a separate metric from equalized odds and must be explicitly enabled in the policy definition.

Exam trap

Salesforce often tests the distinction between equalized odds and equal opportunity, trapping candidates who assume equalized odds automatically includes equal opportunity, when in fact they are separate metrics with different mathematical definitions.

How to eliminate wrong answers

Option A is wrong because demographic parity is explicitly enabled in the JSON policy under the 'fairness_metrics' array. Option B is wrong because not all metrics are enabled; the policy omits equal opportunity. Option C is wrong because disparate impact is also explicitly listed in the policy's fairness metrics.

Practice this question →
73
MCQeasy

A small business uses a pre-built Salesforce AI model to predict inventory needs. The model recommends ordering extra stock based on seasonal trends. One month, the model fails to predict a sudden demand spike, resulting in stockouts and lost sales. The business owner is frustrated and considers disabling the AI. The owner wants to know if this is an ethical issue and what to do next. As an AI ethics advisor, what is the best response?

A.Agree that the AI is unreliable and recommend disabling it immediately
B.Recommend using a different AI model that is guaranteed to be 100% accurate
C.Explain that this is a model performance issue, not an ethics violation, and suggest reviewing the model's accuracy and retraining with recent data
D.State that all AI failures are ethical issues and the company should stop using AI
AnswerC

Ethical issues involve bias, transparency, etc.; a single failure is a reliability issue.

Why this answer

The correct answer is A because a single failure does not necessarily indicate an ethical problem; reliability is a consideration but not an ethics violation. The model should be reviewed and improved. Option B is wrong because blaming the model prematurely is not constructive.

Option C is wrong because ethical concerns are about fairness, transparency, etc., not just accuracy. Option D is wrong because disabling the AI might not be the best solution if the model generally helps.

Practice this question →
74
MCQhard

A company deploys an AI recommender system that personalizes content. The system is trained on user click data. After deployment, the company notices that the system increasingly recommends sensationalist content, leading to user polarization. Which principle is being violated?

A.Accuracy
B.Privacy
C.Beneficence
D.Transparency
AnswerC

The system should promote well-being and avoid harm.

Why this answer

The recommender system's shift toward sensationalist content, which polarizes users, violates the principle of beneficence because it causes harm (user polarization) rather than promoting well-being. Beneficence requires AI systems to act in the best interests of users and society, not to optimize for engagement metrics at the expense of ethical outcomes.

Exam trap

Salesforce often tests the distinction between ethical principles by presenting a scenario where a system functions correctly (accurate) but produces harmful outcomes, leading candidates to mistakenly choose accuracy or transparency instead of beneficence.

How to eliminate wrong answers

Option A is wrong because accuracy refers to the system's ability to make correct predictions or recommendations based on training data, not to the ethical impact of those recommendations; the system may be accurately predicting clicks on sensationalist content. Option B is wrong because privacy concerns unauthorized access or misuse of personal data, whereas the issue here is about the content being recommended, not data exposure. Option D is wrong because transparency involves explainability and openness about how the system works, but the problem is the harmful outcome of the recommendations, not a lack of clarity in the system's logic.

Practice this question →
75
MCQmedium

A healthcare provider uses Einstein's Prediction Builder to predict patient readmission risk. The model outputs a risk score, but clinicians do not understand how the score is calculated. According to ethical AI principles, what should the provider implement?

A.Integrate model explanations using Einstein's Explainability feature to show key factors influencing each prediction.
B.Proceed with the model since it provides accurate predictions.
C.Train only the data science team on the model's inner workings.
D.Replace the model with a simpler, less accurate but fully transparent model.
AnswerA

Explainability supports transparency and human oversight.

Why this answer

Option B is correct because explainability is a key ethical principle, and providing interpretable insights builds trust. Option A is wrong because ignoring the lack of understanding is risky. Option C is wrong because it only provides training to a few.

Option D is wrong because simplifying the model may reduce accuracy, but the core issue is explainability, not complexity.

Practice this question →
Page 1 of 3 · 207 questions totalNext →

Ready to test yourself?

Try a timed practice session using only Ethical Considerations of AI questions.

Start 20-question session