AI Associate Practice Exam 53 — 20 Questions

Question 1

A marketing team at a retail company uses Salesforce Einstein to generate personalized product recommendations. The model analyzes customer browsing history, purchase data, and demographic information including age and gender. A customer support representative receives a complaint from a customer who is concerned about how their data is being used. Which ethical principle is most directly relevant to addressing this customer's concern?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Transparency. The customer's concern centers on understanding how their personal data is used by the AI recommendation system. Transparency is the ethical principle that mandates clear disclosure of data practices, enabling customers to know what data is collected and how it influences AI decisions. By prioritizing transparency, the company can directly address the complaint and build trust, making it the most relevant principle here.

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Accountability

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Fairness

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Reliability

Question 2

A Salesforce developer is building an AI-powered chatbot to handle customer inquiries. The chatbot will have access to customer account details. Which action best aligns with the ethical principle of transparency?

Accepted Answer

Clearly informing users that they are interacting with an AI and providing an option to escalate to a human agent.. Transparency in AI means users should be aware when they are interacting with an AI system and understand its capabilities and limitations. Informing users that they are chatting with a bot and providing a human escalation option ensures they are not misled and can choose an alternative if desired. This practice builds trust and is a fundamental ethical requirement for customer-facing AI.

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Limiting the chatbot's responses to a predefined set of FAQs to ensure accuracy.

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Using a complex neural network to generate responses that mimic human writing style.

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Storing all customer conversations in a secure database for future model training.

Question 3

A Salesforce developer is deploying an AI model that predicts employee attrition. The model uses historical data that includes performance ratings, which are known to be biased against certain demographic groups. Which step should the developer take to address this ethical concern?

Accepted Answer

Audit the performance ratings for bias and adjust the model to use bias-mitigated features.. Auditing performance ratings for bias and applying mitigation techniques directly addresses the ethical concern of discrimination. This ensures the AI model promotes fairness and does not perpetuate historical inequalities. It is a necessary step to align with ethical AI principles and legal requirements.

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Use the model only for advisory purposes and let managers make final decisions.

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Deploy the model as is, since historical data reflects real-world outcomes.

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Remove the performance ratings entirely and rely on other predictors like tenure and salary.

Question 4

A financial institution uses Salesforce Einstein to predict credit risk. The model's decisions are opaque, and customers are not informed about how their creditworthiness is determined. Which ethical principle is most directly compromised?

Accepted Answer

Transparency. The scenario highlights that the AI model's decisions are opaque and customers are not informed about how their creditworthiness is determined. This directly undermines transparency, which is the ethical principle requiring that AI systems be explainable and that individuals understand the basis of decisions affecting them. Transparency is essential for trust, regulatory compliance, and enabling customers to contest decisions. Therefore, transparency is the most directly compromised principle.

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Accountability

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Privacy

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Fairness

Question 5

A Salesforce customer service team plans to deploy an Einstein Bots chatbot that handles billing inquiries. To uphold ethical AI principles, which practice should be implemented when the bot cannot resolve a customer's issue?

Accepted Answer

Escalate the conversation to a human agent and inform the customer.. When an AI chatbot cannot resolve an issue, escalating to a human agent and informing the customer is the ethical choice. It ensures the customer gets help, maintains transparency about the AI's limitations, and upholds human oversight. This approach respects the user and aligns with responsible AI practices that prioritize customer welfare over automation metrics.

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Keep the customer in a loop of automated responses until they give up.

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Ask the customer to call a phone number that is not staffed.

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Have the bot automatically close the case to improve efficiency metrics.

Question 6

A Salesforce administrator at a retail company is deploying an Einstein-powered product recommendation engine. The marketing team wants to use customer browsing history and purchase data to personalize recommendations. Which action best aligns with ethical AI principles regarding transparency and customer trust?

Accepted Answer

Provide customers with a clear, accessible explanation of how their data is used and offer an opt-out mechanism.. Providing a clear explanation and opt-out mechanism upholds transparency and user consent, which are fundamental to ethical AI. Salesforce's responsible AI principles stress the importance of informing users about data usage and giving them control. This builds trust and aligns with regulations like GDPR. The other options either ignore transparency, lack documentation, or fail to provide user choice.

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Deploy the recommendation engine immediately without documentation, as it is an internal tool and not customer-facing.

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Enable the recommendation engine to collect additional data from third-party sources without notifying customers to improve accuracy.

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Use only aggregate data to avoid privacy concerns, even if it reduces the personalization quality.

Question 7

A Salesforce developer is integrating Einstein Language services to analyze customer feedback. Which TWO actions align with ethical AI principles when handling the data? (Choose two.)

Accepted Answer

Anonymize personally identifiable information before sending text to the AI service.. Anonymizing PII and obtaining informed consent are both essential for ethical AI. Anonymization protects privacy by removing identifiers, while consent ensures customers agree to the use of their data. Together, they uphold transparency, data minimization, and respect for user autonomy, aligning with responsible AI principles and privacy regulations.

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Share the feedback data with third-party advertisers to monetize the AI service.

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Use the feedback to train models without notifying customers, as long as it improves service.

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Store the raw customer feedback indefinitely for future model training.

Question 8

A Salesforce admin is configuring Einstein Trust Layer for a generative AI feature in Service Cloud. The company's legal team wants to ensure that sensitive customer data, such as credit card numbers, is not sent to the large language model (LLM) provider. Which Salesforce feature should the admin enable to achieve this?

Accepted Answer

Data Masking. Einstein Trust Layer includes Data Masking, which automatically identifies and masks sensitive data such as credit card numbers before prompts are sent to the LLM. This ensures that external AI services never receive sensitive information, addressing privacy and ethical concerns. Other options do not provide this specific pre-processing masking capability.

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Field-Level Encryption

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Shield Platform Encryption

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Einstein Data Detection

Question 9

A data scientist is building an AI model on Salesforce to predict customer churn. The model uses historical data that includes sensitive attributes such as age, gender, and postal code. During testing, the model shows high accuracy but disproportionately predicts churn for older customers. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness. The model's disproportionate impact on older customers is a clear case of bias, violating the ethical principle of fairness. Fairness demands that AI systems treat all groups equitably and avoid discrimination. While transparency, accountability, and privacy are important, they do not directly address the discriminatory outcome. Salesforce's responsible AI principles prioritize fairness and bias mitigation, making it the correct answer.

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Accountability

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Privacy

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Transparency

Question 10

A Salesforce administrator is implementing Einstein AI for customer service chatbots. To ensure ethical AI practices, which two actions should be taken? (Choose two.)

Accepted Answer

Regularly audit the chatbot's responses for bias and accuracy.. Regularly auditing the chatbot for bias and accuracy ensures fairness and accountability, while disclosing AI interaction upholds transparency. These actions align with Salesforce's responsible AI principles, which emphasize fairness, transparency, and accountability. Together, they help build trust and mitigate ethical risks in AI deployment.

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Use only synthetic data for training to avoid any privacy concerns.

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Deploy the chatbot without testing to quickly gather user feedback.

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Allow the chatbot to access all customer data without restrictions to improve personalization.

Question 11

A Salesforce administrator at a retail company is configuring Einstein Trust Layer for a new customer service chatbot that uses an external large language model (LLM). The administrator enables Data Masking and Zero Data Retention. During testing, the chatbot still includes a customer's full name and account number in its generated response. Which ethical principle is most directly being compromised by this behavior?

Accepted Answer

Privacy. The scenario describes a failure to protect personal data despite privacy controls, so the ethical principle most directly compromised is privacy. Data Masking and Zero Data Retention are meant to prevent sensitive information from being exposed, but the chatbot still outputs a full name and account number. This is a clear privacy violation, not primarily an issue of transparency, accountability, or inclusiveness.

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Transparency

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Accountability

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Inclusiveness

Question 12

A financial services company uses Einstein Prediction Builder to predict loan default risk. The model is trained on historical loan data that includes zip code as a feature. An audit reveals that the model's predictions disproportionately flag applicants from low-income neighborhoods as high risk, even when controlling for credit score and income. Which ethical issue is most directly implicated?

Accepted Answer

Proxy discrimination. The model's use of zip code, which correlates with low-income neighborhoods, results in disproportionate high-risk predictions for applicants from those areas. This is proxy discrimination because zip code acts as a proxy for socioeconomic status or race, leading to unfair treatment. Even if the model does not explicitly use protected attributes, the outcome is discriminatory. The other options, while relevant to AI ethics, do not capture the specific harm of disparate impact caused by a correlated feature.

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Informed consent breach

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Lack of explainability

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Data minimization violation

Question 13

A data scientist at a healthcare company is using Salesforce Einstein Discovery to predict patient readmission risks. The model uses historical patient data, including socioeconomic status. The data scientist notices that the model's predictions are less accurate for patients from lower socioeconomic backgrounds. Which ethical principle is being violated?

Accepted Answer

Fairness. The model's reduced accuracy for patients from lower socioeconomic backgrounds indicates unequal performance, which is a fairness violation. Fairness in AI requires equitable treatment and outcomes across groups, and this model fails to provide reliable predictions for all patients. Addressing this requires bias detection and mitigation to ensure the model serves all demographics equitably, aligning with ethical AI principles.

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Reliability

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Accountability

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Transparency

Question 14

A data scientist at a financial services company is using Salesforce Einstein Discovery to build a model that predicts customer churn. The model uses historical customer data including age, income, and postal code. After deployment, the compliance team notices that the model's predictions disproportionately favor customers from affluent neighborhoods. Which ethical principle is most directly violated by this outcome?

Accepted Answer

Fairness, because the model produces biased outcomes against certain socioeconomic groups.. The model's predictions disproportionately favor affluent neighborhoods, which is a clear case of algorithmic bias. Fairness in AI demands that models do not create or reinforce discrimination against protected groups. While transparency, accountability, and reliability are also important ethical principles, the specific violation here is fairness, as the model produces inequitable outcomes based on socioeconomic status.

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Transparency, because the model's decision-making process is not explainable to customers.

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Accountability, because no one is responsible for the model's predictions.

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Reliability, because the model's predictions are not consistently accurate.

Question 15

A company uses Salesforce Einstein to predict employee attrition. The model's recommendations lead to managers taking preemptive actions that disproportionately affect older employees. Which ethical principle is most directly violated?

Accepted Answer

Fairness. The scenario describes an AI system whose recommendations lead to actions that disproportionately affect older employees, which is a clear case of age discrimination. This violates the ethical principle of fairness, which demands that AI systems treat all individuals equitably and do not disadvantage protected groups. Fairness is a core component of Salesforce's responsible AI principles, and addressing this would require bias testing and mitigation.

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Accountability

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Privacy

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Transparency

Question 16

A company uses an Einstein Discovery model to recommend which customers should receive a retention offer. The model was trained on historical data where offers were disproportionately given to male customers. To mitigate bias, the data science team plans to use a fairness metric. Which metric would best detect disparate impact against female customers?

Accepted Answer

Demographic parity difference. Demographic parity difference directly compares the proportion of positive outcomes between groups. If female customers receive retention offers at a lower rate than male customers, this metric will show a disparity, indicating potential bias. It is a standard fairness metric for detecting disparate impact and aligns with ethical AI practices of monitoring group outcomes.

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Mean squared error

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Precision-recall AUC

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Overall model accuracy

Question 17

A Salesforce developer is integrating an AI chatbot into a customer service portal. The chatbot uses natural language processing to handle customer inquiries. To ensure ethical AI, which practice should be prioritized during the design phase?

Accepted Answer

Implement clear disclosure that the user is interacting with an AI and provide an option to escalate to a human agent.. Disclosing AI interaction and offering human escalation are fundamental ethical practices. They ensure transparency, respect user choice, and prevent deception. Salesforce's ethical AI guidelines emphasize transparency and user control. The other options either violate privacy, deceive users, or collect data without consent, all of which are unethical. Thus, clear disclosure and escalation options are the most ethical design choices.

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Train the chatbot to mimic human emotions to create a more engaging user experience.

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Use the chatbot to collect additional personal data, such as phone numbers, without explicit consent to improve future interactions.

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Ensure the chatbot can access all customer data to provide personalized responses.

Question 18

A Salesforce admin is setting up Einstein Trust Layer for a new generative AI feature. The company's ethical AI policy requires that all AI-generated content be reviewed for toxic or harmful language before being shown to customers. Which Einstein Trust Layer capability should the admin enable?

Accepted Answer

Toxicity Detection. Toxicity Detection is the Einstein Trust Layer feature that identifies and mitigates harmful or offensive language in AI-generated responses. By enabling it, the admin ensures that customers are not exposed to toxic content, aligning with the company's ethical AI policy. Other features address different concerns such as data privacy or auditability.

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Zero Data Retention

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Audit Trail

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Data Masking

Question 19

A Salesforce administrator at a retail bank configures Einstein Trust Layer for a new customer service agent that summarizes case notes. The administrator enables data masking for social security numbers and credit card numbers. Which ethical principle does this configuration primarily uphold?

Accepted Answer

Privacy. Data masking in Einstein Trust Layer protects sensitive personal information from being exposed to or processed by the AI model. This directly supports privacy by preventing unauthorized access to social security and credit card numbers. While transparency, accountability, and fairness are also ethical principles, the specific action of masking sensitive data is most closely tied to privacy. The configuration ensures that the AI system does not handle raw sensitive data, reducing risk of data breaches.

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Fairness

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Accountability

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Transparency

Question 20

A Salesforce admin is preparing a dataset for an Einstein Discovery model that predicts customer churn. The dataset contains a 'LastPurchaseDate' field, and the admin wants to transform it into a numeric feature representing the number of days since the last purchase relative to today. Which approach should the admin use to create this feature?

Accepted Answer

Create a formula field on the source object that calculates the difference between TODAY() and LastPurchaseDate, then include it in the dataset.. Creating a formula field that subtracts LastPurchaseDate from TODAY() produces a numeric feature representing recency, which is critical for churn prediction. This method is dynamic, automatically updated, and integrates directly with Einstein Discovery datasets. It avoids manual intervention and ensures the model always uses current data.

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Configure a workflow rule to update a numeric field with the day difference whenever the record is edited.

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Use a roll-up summary field to count the number of days between the last purchase and the current date.

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Manually export the data, calculate the difference in a spreadsheet, and re-import it as a static field.