AI Associate Practice Test 12 — 15 Questions

Question 1

A company wants to provide personalized product recommendations on their community site built with Experience Cloud. Which Einstein feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the Salesforce feature designed to generate personalized recommendations on Experience Cloud sites based on user behavior and data. It uses machine learning to suggest products, articles, or content tailored to each visitor, which directly matches the requirement for personalized product recommendations on a community site.

Answer

Einstein Prediction Builder

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Einstein Vision

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Einstein Next Best Action

Question 2

Which TWO actions can be performed using Einstein Activity Capture?

Accepted Answer

Automatically log emails from Outlook or Gmail. Einstein Activity Capture is a Salesforce feature that automatically syncs a user's Microsoft 365 (Outlook) or Google Workspace (Gmail) email and calendar activity into Salesforce, so option A is correct because it automatically logs emails from Outlook or Gmail, and option E is correct because it automatically logs meetings from calendar events. These two capabilities are the core purpose of Einstein Activity Capture: it captures email messages and calendar events and associates them with the relevant Salesforce records (leads, contacts, accounts, opportunities) without manual data entry. The unmarked options do not belong because Einstein Activity Capture does not parse email content to update opportunity amounts (B), does not create tasks from email attachments (C), and does not generate leads from email signatures (D); those would require additional automation such as Flow, Apex, or Einstein features beyond Activity Capture's native sync.

Answer

Update opportunity amounts based on email content

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Create tasks from email attachments

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Generate leads from email signatures

Question 3

A company wants to deploy an AI system that makes hiring decisions. To comply with ethical guidelines, what should they do before deployment?

Accepted Answer

Conduct an ethics review and perform bias testing on diverse datasets.. Conducting an ethics review and performing bias testing on diverse datasets are essential steps to identify and mitigate potential discriminatory outcomes in AI-driven hiring systems. This aligns with ethical AI frameworks that require fairness, accountability, and transparency before deployment, ensuring the model does not perpetuate historical biases or violate anti-discrimination laws.

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Ensure the system achieves high accuracy and ignore other metrics.

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Deploy immediately and monitor for issues.

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Test the system only on a small dataset to expedite launch.

Question 4

Which Einstein feature provides AI-powered predictions for opportunity win likelihood?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring uses AI to analyze historical opportunity data and predict the likelihood that an open opportunity will be won. It surfaces a score (typically 1–100) on the opportunity record so reps can prioritize deals. This is distinct from Lead Scoring, which predicts conversion of leads, not opportunities.

Answer

Einstein Forecasting

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Einstein Lead Scoring

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Einstein Prediction Builder

Question 5

A company uses Einstein Discovery to analyze sales data. They want to understand the key drivers of deal closures. Which THREE output types can Einstein Discovery provide? (Choose 3)

Accepted Answer

Stories that explain key factors in natural language. Option A is correct because Einstein Discovery generates Stories that present the top predictors and insights behind an outcome (such as deal closure) in plain, natural-language explanations. Option B is correct because Einstein Discovery produces prescriptions—actionable recommendations that suggest the best next steps or changes to improve a predicted outcome, which directly addresses understanding what drives closures. Option C is correct because Einstein Discovery visualizes how each individual factor pushes a prediction up or down using waterfall charts, showing the contribution of each variable to the outcome. Option D is not correct because sentiment analysis is a separate natural-language processing capability (e.g., Einstein Sentiment), not a core Einstein Discovery output type. Option E is not correct because custom binary prediction models are built with Einstein Prediction Builder or custom model development, not a standard output type of Einstein Discovery.

Answer

Sentiment analysis scores

Answer

Custom binary prediction models

Question 6

A company has a custom AI model for sentiment analysis and wants to use it in Salesforce without rebuilding. Which approach should they take?

Accepted Answer

Use Bring Your Own Model (BYOM) for Einstein. Bring Your Own Model (BYOM) for Einstein allows companies to deploy their own pre-trained AI models directly into Salesforce without rebuilding them. This approach leverages Salesforce's infrastructure for inference while keeping the custom model intact, making it the ideal solution for integrating a custom sentiment analysis model.

Answer

Use Data Export and import into external system

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Use MuleSoft

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Build in Apex

Question 7

Refer to the exhibit. An admin configures Einstein Next Best Action with the above JSON. The expected behavior is to recommend the top 5 actions for open leads with a score of at least 70. However, only 2 recommendations appear for some leads. Which is the most likely cause?

Accepted Answer

The scoreThreshold of 70 excludes many actions, so fewer than 5 meet the criteria.. The scoreThreshold of 70 filters out any actions with a score below 70. If fewer than 5 actions meet this threshold, the system returns only those that qualify, resulting in fewer than 5 recommendations. The maxRecommendations setting defines the upper limit, but the actual number returned is constrained by the scoreThreshold.

Answer

The filter on Lead Status is incorrectly excluding actions.

Answer

The recommendation strategy is misconfigured for this object.

Answer

The maxRecommendations is set to 2 instead of 5.

Question 8

A service manager wants to automatically categorize incoming cases into standard fields like Type, Priority, and Reason based on historical case data. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification automatically predicts standard case fields like Type, Priority, and Reason based on historical case data and the case description. It is specifically designed to categorize incoming cases into these standard fields. This matches the service manager's requirement exactly.

Answer

Einstein Discovery

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Einstein Prediction Builder

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Einstein Article Recommendations

Question 9

A company wants to use Einstein Discovery to analyze sales data and automatically uncover key drivers of deal closure. What must the admin provide to create a story?

Accepted Answer

At least one numeric field to predict. Einstein Discovery requires at least one numeric field as the prediction target (e.g., deal amount, probability score) to train its regression or classification model. Without a numeric field to predict, the story cannot define what outcome the AI should analyze or uncover key drivers for.

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A date field for time series analysis

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A foreign key to relate objects

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A text field for sentiment analysis

Question 10

A company uses AI to generate personalized email content for marketing campaigns. They notice the AI occasionally produces factually incorrect statements. Which THREE actions should they take to mitigate this?

Accepted Answer

Implement retrieval-augmented generation (RAG) to ground outputs in verified data. Option A is correct because retrieval-augmented generation (RAG) retrieves relevant, verified documents at inference time and injects them into the prompt, grounding the model's output in authoritative source data and directly reducing fabricated statements (hallucinations). Option B is correct because lowering the temperature parameter reduces sampling randomness, making the model favor higher-probability tokens and produce more deterministic, conservative outputs that are less likely to drift into invented facts. Option C is correct because a human-in-the-loop review step catches residual factual errors before emails reach customers, providing a final verification layer that neither RAG nor temperature tuning can fully guarantee. Option D is not appropriate because training on more unlabeled internet emails adds noisy, unverified data that can increase hallucination and bias rather than improve factual accuracy. Option E is not appropriate because removing safety guardrails increases the risk of harmful or inappropriate content and does nothing to improve factual correctness.

Answer

Train the model on more unlabeled emails from the internet

Answer

Remove all safety guardrails to allow more creative content

Question 11

Which Salesforce AI feature provides audit logging of when AI recommendations are generated and acted upon?

Accepted Answer

Einstein Trust Layer audit trail. The Einstein Trust Layer includes an audit trail that logs when AI recommendations are generated and when users act on them. This audit trail is essential for compliance and governance, as it records the AI's decision-making process and user interactions, ensuring transparency and accountability in AI-driven recommendations.

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Einstein Discovery

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Einstein Copilot

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Einstein Prediction Builder

Question 12

A sales rep wants to automatically log emails from Microsoft Outlook to Salesforce without manual forwarding. Which feature should the admin enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (EAC) is the correct feature because it automatically syncs emails and events from Microsoft Outlook (or Google) into Salesforce without requiring manual forwarding or BCC. It uses a background synchronization service that captures email metadata and content based on configured rules, enabling automatic logging directly to related Salesforce records.

Answer

Einstein GPT for Sales

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Einstein Conversation Insights

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Einstein Email Insights

Question 13

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

Accepted Answer

Accountability. According to Salesforce's guidance on ethical AI, Accountability (A) is a key consideration because organizations must be able to explain and take responsibility for their AI systems' decisions and outcomes, ensuring human oversight and clear ownership. Transparency (C) is also essential, as AI systems should be understandable and their decision-making processes open to scrutiny, so users and stakeholders can trust and audit them. Privacy (D) is a core ethical consideration because AI must protect personal data, respect consent, and comply with regulations, preventing misuse or unauthorized exposure of sensitive information. The unmarked options do not belong: Profitability (B) is a business objective rather than an ethical principle, and Speed (E) is a performance metric, not an ethical requirement for responsible AI.

Answer

Profitability

Answer

Speed

Question 14

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. The performance disparity across demographic groups, as shown in the exhibit, directly indicates a fairness issue. Option A is incorrect because privacy concerns are not directly indicated by the performance differences. Option B is incorrect because the audit's independence is not mentioned; the issue is about fairness, not accountability. Option C is incorrect because overall accuracy is high, but group fairness is lacking, which is a fairness issue, not transparency. Option D is correct because the model's worse performance for the minority group violates fairness principles.

Answer

Privacy: the data includes sensitive attributes

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Accountability: the audit was not independent

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Transparency: the model's overall accuracy is too low

Question 15

A team is developing a chatbot for customer service. To ensure ethical AI, which practice should be incorporated?

Accepted Answer

Allow the chatbot to escalate to a human agent upon request.. Human escalation supports accountability and user control. Option B is wrong because indefinite data storage violates privacy. Option C is wrong because a single data source may introduce bias. Option D is wrong because mimicking emotions may deceive users.

Answer

Store all conversation data indefinitely for analysis.

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Use a single data source for training to avoid inconsistency.

Answer

Design the chatbot to mimic human emotions perfectly.