AI Associate Ethical Considerations of AI Practice Test 12 — 15 Questions

Question 1

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

Accepted Answer

Investigate causality before action. Correlation does not imply causation; responsible use involves investigating the underlying cause before acting.

Answer

Act on the correlation immediately

Answer

Discard the result

Answer

Publish the result as-is

Question 2

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

Accepted Answer

Provide human oversight. Human oversight (Option B) is essential because it ensures that AI decisions can be reviewed, overridden, or corrected by a person, which is a core requirement of ethical AI frameworks such as the EU AI Act and NIST AI Risk Management Framework. This oversight helps catch biased outputs, edge cases, or harmful actions that the model might produce, maintaining accountability and safety.

Answer

Avoid transparency

Answer

Use the most complex model

Answer

Automate all decisions

Question 3

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

Accepted Answer

Audit the training data for bias. Auditing training data, retraining with balanced data, and monitoring outcomes are essential corrective actions.

Answer

Deploy the model without changes

Answer

Remove demographic data from the model

Question 4

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

Accepted Answer

The bot should disclose it is an AI. Ethical AI guidelines, including those from Salesforce, require that bots disclose their non-human identity to users. This transparency builds trust and ensures users are aware they are interacting with an AI, not a human, which is a core ethical principle in AI deployment.

Answer

The bot should mimic a human

Answer

The bot should make decisions autonomously

Answer

The bot should not collect personal data

Question 5

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

Accepted Answer

Equal opportunity. (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.

Answer

Demographic parity

Answer

All are enabled

Answer

Disparate impact

Question 6

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

Accepted Answer

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

Answer

Group Alpha

Answer

Group Gamma

Answer

Group Delta

Question 7

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

Accepted Answer

To detect biased predictions based on gender and race. The configuration enables bias detection on the specified sensitive attributes (gender and race).

Answer

To block all predictions involving gender or race

Answer

To anonymize gender and race data

Answer

To remove gender and race from the model

Question 8

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 9

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

Accepted Answer

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

Answer

Reduce the cost of agent training.

Answer

Maximize the number of recommendations.

Answer

Increase the speed of case resolution.

Question 10

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?

Accepted Answer

Einstein Trust Layer.. 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.

Answer

Permission Set.

Answer

Einstein Analytics.

Answer

Data Mask.

Question 11

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

Accepted Answer

Document the model's intended use and limitations.. Documenting intended use (transparency) and providing a challenge mechanism (accountability) align with ethical AI principles.

Answer

Use only structured data for training.

Answer

Use the model to make decisions without human review.

Answer

Optimize for accuracy over all other metrics.

Question 12

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

Accepted Answer

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

Answer

To improve the accuracy of sentiment analysis.

Answer

To reduce latency of the AI response.

Answer

To comply with Salesforce's service-level agreement.

Question 13

A developer is creating a custom AI model on Salesforce. To ensure the model is fair across demographic groups, which activity should be included in the development process?

Accepted Answer

Bias testing using a diverse test dataset.. Bias testing using diverse datasets directly evaluates fairness across groups.

Answer

Feature selection using correlation matrix.

Answer

Cross-validation to avoid overfitting.

Answer

Hyperparameter tuning with grid search.

Question 14

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?

Accepted Answer

Review the training data for geographic bias.. 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.

Answer

Contact Salesforce support for a refund.

Answer

Increase the number of recommendations shown.

Answer

Disable the recommendation engine immediately.

Question 15

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

Accepted Answer

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

Answer

Profitability.

Answer

Scalability.