AI Associate Practice Test 37 — 15 Questions

Question 1

A company is implementing Einstein Next Best Action for their customer service agents. They want to ensure that AI recommendations are provided as suggestions but that agents retain the final say. Which TWO practices support this goal of human oversight?

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

Accepted Answer

Configure the system to require agent confirmation before executing any AI-recommended action. Option D is correct because configuring the system to require explicit agent confirmation before executing any AI-recommended action enforces a human-in-the-loop control, ensuring the agent authorizes every action rather than the AI acting autonomously. Option E is correct because displaying AI recommendations as optional suggestions that agents can accept or ignore directly implements human oversight, keeping the agent as the decision-maker while still benefiting from AI guidance. Together, these practices align with Einstein Next Best Action's design of surfacing recommendations while preserving agent discretion. Option A is wrong because disabling all AI recommendations eliminates the AI value entirely rather than providing suggestions with oversight. Option B, while useful for adoption, is a training activity and does not itself enforce human oversight of AI actions. Option C is wrong because automating top recommendations removes the agent's final say, contradicting the stated goal.

Answer

Disable all AI recommendations and rely solely on agent judgment

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Train all agents on how AI models work

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Automate the top recommendations to save agent time

Question 2

Which of the following is a key feature of Salesforce Einstein Trust Layer that protects customer data when using AI?

Accepted Answer

Zero data retention. The Salesforce Einstein Trust Layer is designed with a zero data retention policy, meaning that once an AI model processes a request and returns a response, Salesforce does not store the customer's data. This ensures that sensitive information is not retained on Salesforce servers, protecting customer privacy and complying with data protection regulations. The zero data retention feature is a core architectural principle that prevents data leakage from AI interactions.

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Automatic model retraining

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Data masking for all PII

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Open-source model deployment

Question 3

Which Salesforce feature allows administrators to mask personally identifiable information (PII) in prompts sent to large language models?

Accepted Answer

Einstein Trust Layer. The Einstein Trust Layer is a security architecture within Salesforce that intercepts prompts sent to large language models (LLMs) and automatically masks personally identifiable information (PII) before the data reaches the external AI service. After the LLM returns a response, the Trust Layer reinserts the masked data, ensuring that sensitive information never leaves the Salesforce ecosystem in plain text. This feature is specifically designed to address data privacy and compliance requirements when using generative AI features like Einstein GPT.

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Sharing Rules

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Permission Sets

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Data Export Service

Question 4

A sales rep wants to automatically generate a personalized email draft to a lead based on recent account activity. Which Einstein feature should be used?

Accepted Answer

Einstein GPT - Sales GPT. Sales GPT, part of Einstein GPT, is specifically designed to generate personalized email drafts using natural language generation (NLG) based on CRM data such as recent account activity. It leverages generative AI to create context-aware content, unlike other Einstein features that focus on prediction, scoring, or data capture.

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Einstein Activity Capture

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Einstein Recommendation Builder

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Einstein Opportunity Scoring

Question 5

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

Accepted Answer

Ensure the training data is representative of the entire customer base.. 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.

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Focus solely on model accuracy ignoring demographic groups.

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Remove all demographic attributes from the dataset.

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Use only historical data without checking for bias.

Question 6

Refer to the exhibit. Which ethical principle is most at risk based on this AI governance configuration?

Accepted Answer

Accountability. Accountability, because an AI governance configuration that lacks clear ownership, oversight, or defined responsibility for AI decisions leaves no party answerable for the system's outcomes, which is precisely the accountability principle at risk. Accountability requires assigned roles, audit trails, and mechanisms to hold someone responsible for AI behavior, and a governance setup missing these elements directly undermines it. Transparency (A) concerns disclosure and explainability of how the AI works, Fairness (B) concerns bias and equitable treatment, and Privacy (C) concerns protection of personal data, so while each is a distinct ethical principle, none is the one most directly threatened by a governance configuration that fails to establish responsibility.

Answer

Transparency

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Fairness

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Privacy

Question 7

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.

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Permission Set.

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Einstein Analytics.

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Data Mask.

Question 8

A nonprofit uses Einstein Vision to classify images of disaster areas. What is the primary benefit of using AI for this task?

Accepted Answer

It reduces manual effort and speeds up damage assessment.. Einstein Vision automates the classification of disaster images, significantly reducing the manual effort required for damage assessment. By processing large volumes of images rapidly, it accelerates the time to insight, enabling faster response and resource allocation. This aligns with the core benefit of AI: augmenting human effort with speed and scale.

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It requires less training data than manual methods.

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It eliminates all classification errors.

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It can only classify images of specific disaster types.

Question 9

A retailer is using Einstein Product Recommendations on their ecommerce site. They want to avoid biased recommendations that might disadvantage certain customer groups. Which THREE steps should they take?

Accepted Answer

Provide customers with a reason why a particular product was recommended.. To avoid biased recommendations, the retailer should audit outputs for disparate impact (B), include diverse training data to represent all groups (E), and provide customers with reasons for recommendations to ensure transparency (A). Removing all demographic data from model features (C) is not recommended because it can prevent detection of bias and may not eliminate it; instead, the goal is to ensure fair representation and accountability.

Answer

Remove all customer demographic data from the model features.

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Use only historical purchase data to train the model.

Question 10

A company uses Einstein Prediction Builder to predict customer churn. They notice the model is less accurate for a particular demographic group. According to ethical AI principles, what should the company do first?

Accepted Answer

Conduct a bias audit to assess the model's fairness across demographics. Conduct a bias audit to assess the model's fairness across demographics. When a model like Einstein Prediction Builder shows disparate accuracy for a demographic group, ethical AI principles require first identifying and quantifying the source and extent of the bias before taking corrective action, which a structured bias audit provides. Retraining with more data from that group only (B) is premature because it may not address the root cause and could introduce further imbalance, while ignoring the discrepancy (C) or switching models without investigation (D) both skip the required diagnostic step and risk perpetuating or masking unfair outcomes.

Answer

Retrain the model with more data from that demographic group only

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Ignore the discrepancy as it might be due to random variation

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Switch to a different AI model without investigating

Question 11

A healthcare organization uses Salesforce to manage patient records. They want to deploy an AI system that predicts patient readmission risk. Which practice BEST ensures ethical use of patient data?

Accepted Answer

Obtain explicit patient consent and anonymize data for training. Obtaining explicit patient consent and anonymizing data before training directly addresses the ethical principles of autonomy, privacy, and data minimization required for handling protected health information under regulations like HIPAA. Anonymization reduces re-identification risk, while explicit consent ensures patients understand and authorize how their data is used for AI model development. Option A is unethical and likely illegal, as using a readmission-risk model to deny coverage constitutes discriminatory and non-transparent decision-making. Option B ignores data provenance and consent, which are essential to ethical AI even if accuracy is high. Option D is risky because deploying a third-party tool without reviewing its data practices can lead to unauthorized use or exposure of patient data.

Answer

Use the model to deny high-risk patients coverage

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Focus solely on model accuracy regardless of data source

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Deploy a third-party AI tool without reviewing its data practices

Question 12

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

Accepted Answer

Ensuring training data is diverse and representative. Option A is correct because ensuring training data is diverse and representative directly addresses the root cause of many biases: skewed or unrepresentative datasets that cause the model to learn and reproduce discriminatory patterns. Option B is correct because conducting regular bias audits on model outcomes allows teams to detect disparate impact across demographic groups after deployment and take corrective action, which is a key part of responsible AI governance. Option E is correct because involving a diverse team in model development brings varied perspectives that help identify blind spots, assumptions, and edge cases that a homogeneous team might overlook. Option C is not correct because reducing model complexity affects performance, interpretability, and overfitting, but it does not inherently mitigate bias and can even worsen it by limiting the model's ability to capture fair representations. Option D is not correct because using only historical data without modification preserves and can amplify existing societal biases embedded in that data, which is the opposite of a bias-mitigation strategy.

Answer

Reducing the complexity of the model

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Using only historical data without modification

Question 13

A retail company uses Einstein to personalize product recommendations. The AI model is trained on customer purchase data that includes sensitive attributes like race and gender. The company wants to ensure ethical use. Which action would best address fairness concerns?

Accepted Answer

Remove race and gender fields from the training dataset. Removing sensitive attributes like race and gender from the training dataset helps prevent the model from directly using these factors to discriminate, thereby reducing fairness concerns. Option B is incorrect because obtaining consent does not address bias; it only addresses privacy. Option C is incorrect because adding more demographic data could amplify bias if that data is correlated with sensitive attributes. Option D is incorrect because randomizing recommendations sacrifices personalization and does not correct underlying bias in the model.

Answer

Obtain explicit consent from customers for data use

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Add more demographic data to improve model accuracy

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Randomize recommendations to ensure equal treatment

Question 14

A sales rep wants Einstein to automatically capture emails and calendar events to Salesforce without manual logging. Which feature enables this?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture automatically syncs emails and calendar events from connected email and calendar systems into Salesforce, associating them with the right records without manual logging. This is exactly the feature that enables automatic capture of emails and events.

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Einstein Email Insights

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Einstein Conversation Insights

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Einstein GPT for Sales

Question 15

A marketing manager uses Einstein recommendations on their website, but customers are receiving suggestions for products they already purchased. What is the most likely cause?

Accepted Answer

The recommendation model is not filtering out previously purchased items.. C is correct because the most likely cause is that the recommendation model is not configured to exclude previously purchased items. Einstein Recommendations uses customer purchase history to personalize suggestions, but if the model's filtering logic does not explicitly remove items the customer has already bought, those items will continue to appear in the recommendations. This is a common oversight in model configuration rather than a data sync or algorithm type issue.

Answer

The product catalog is not updated with purchase history.

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The model is using real-time browsing data that includes past purchases.

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The recommendations are based on collaborative filtering without personalization.