AI Associate Practice Test 14 — 15 Questions

Question 1

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

Accepted Answer

Potential for geographic discrimination due to zip code condition. Option C is correct: The use of zip_code as an approval condition can lead to geographic discrimination (redlining). Option A is wrong because the rules are transparent (explicitly shown). Option B is wrong because income and credit score may be relevant, but zip code is problematic. Option D is wrong because the rules are defined, but accuracy is not directly addressed.

Answer

Use of irrelevant attributes like income and credit score

Answer

Insufficient accuracy due to simple rules

Answer

Lack of transparency in decision-making

Question 2

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?

Accepted Answer

Develop a centralized AI ethics framework that applies to all Salesforce AI services and train staff on its principles. 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.

Answer

Limit AI usage to only one Salesforce AI service to simplify oversight

Answer

Allow each business unit to define its own ethical guidelines for its respective AI service

Answer

Rely on existing legal and regulatory compliance requirements to guide ethical use

Question 3

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

Accepted Answer

Augment data with synthetic samples for underrepresented groups.. 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.

Answer

Remove the underrepresented groups from the scope.

Answer

Increase model regularization.

Answer

Use the data as is, as the model will generalize.

Question 4

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

Accepted Answer

Retrain the model with a balanced dataset that includes more leads from the under-scored region.. Retraining with a balanced dataset helps mitigate bias by ensuring the model learns from a representative sample, aligning with fairness principles.

Answer

Switch to a different AI vendor.

Answer

Ignore the bias because the model is proprietary.

Answer

Remove the region field from the model entirely.

Question 5

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

Accepted Answer

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

Answer

Stop word exclusion.

Answer

Query performance optimization.

Answer

Tokenization settings.

Question 6

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

Accepted Answer

Use adversarial debiasing technique. 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.

Answer

Increase model regularization

Answer

Remove the gender feature

Answer

Reduce model complexity

Question 7

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?

Accepted Answer

Implement a fairness metric to evaluate bias.. 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.

Answer

Remove gender-related features from the model.

Answer

Re-train the model with only male candidates.

Answer

Increase the model's training data size.

Question 8

Which THREE actions align with Salesforce's responsible AI principles?

Accepted Answer

Involving diverse stakeholders in AI development.. Options A, C, and E are correct. Monitoring for bias (A) ensures ongoing fairness, involving diverse stakeholders (C) reduces blind spots, and human oversight (E) ensures accountability. Option B is wrong because restricting data sources may limit representativeness. Option D is wrong because accuracy should not override fairness.

Answer

Using only internal data sources.

Answer

Prioritizing model accuracy over fairness.

Question 9

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

Accepted Answer

The training data is imbalanced, which may cause the model to be less accurate for female leads, leading to unfair prioritization.. Option A is correct because the imbalance can lead to the model performing poorly for female leads, causing gender bias. Option B is wrong because the concern is not that accuracy is already biased, but that training data is biased. Option C is wrong because the dataset size is not the main issue; the imbalance is. Option D is wrong because using historical data is not inherently unethical, but the imbalance is problematic.

Answer

The model will be biased against male leads because they are overrepresented.

Answer

The dataset is too small to build a reliable model.

Answer

Using historical data is unethical because it may not reflect current conditions.

Question 10

An AI system used for medical diagnosis occasionally produces incorrect results. A doctor notices the errors but continues using the system without reporting them. Which ethical principle is primarily at risk?

Accepted Answer

Accountability. Option A is correct: Accountability means humans must oversee AI decisions and report issues. Option B is wrong because transparency is about disclosure. Option C is wrong because fairness is about bias. Option D is wrong because privacy is about data protection.

Answer

Fairness

Answer

Transparency

Answer

Privacy

Question 11

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

Accepted Answer

Fairness: the model performs worse for the minority group. Option A is correct: The significant disparity in accuracy and error rates indicates bias, which violates fairness. Option B is wrong because overall accuracy is high, but group fairness is lacking. Option C is wrong because accountability is about responsibility, but the exhibit directly shows unfairness. Option D is wrong because privacy is not directly indicated.

Answer

Privacy: the data includes sensitive attributes

Answer

Accountability: the audit was not independent

Answer

Transparency: the model's overall accuracy is too low

Question 12

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?

Accepted Answer

Investigate whether postal code is a proxy for protected attributes and, if so, consider retraining the model without it or with fairness constraints. 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.

Answer

Remove the postal code field from the model immediately

Answer

Add more demographic data to the model to improve its accuracy

Answer

Ignore the correlation since the model is predicting business value, not demographic attributes

Question 13

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

Accepted Answer

Conduct a bias audit and take corrective action.. 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.

Answer

Obtain consent from applicants.

Answer

Publish the algorithm.

Answer

Discontinue use of the system.

Question 14

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

Accepted Answer

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

Answer

Cost of implementation

Answer

Model accuracy

Answer

Transparency of predictions

Question 15

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

Accepted Answer

Accountability. 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.

Answer

Profitability

Answer

Speed

AI Associate Practice Test 14 — 15 Questions

Exhibit

AI Associate Practice Test 14 — 15 Questions

Exhibit