AI Associate Practice Test 10 — 10 Questions

Question 1

A customer service agent needs to quickly summarize a lengthy case history before responding. Which Einstein GPT feature is designed for this?

Accepted Answer

Service GPT. Service GPT is the correct answer because it is the Einstein GPT feature specifically designed for service agents to quickly summarize lengthy case histories, generate knowledge articles, and draft responses. It leverages generative AI to analyze case data and produce concise summaries, enabling faster and more informed customer interactions.

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Sales GPT

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Einstein Copilot

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Einstein Email Insights

Question 2

A company wants to automatically log emails and calendar events from a sales rep's Office 365 account to Salesforce. Which feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture is the correct feature because it automatically logs emails and calendar events from a sales rep's Office 365 account to Salesforce without requiring manual data entry or complex integrations. It uses a background sync process that captures activities from connected email and calendar systems and maps them to the appropriate Salesforce records, ensuring a seamless flow of communication data into the CRM.

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Einstein Email Insights

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Einstein Discovery

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Einstein Conversation Insights

Question 3

A company is using Einstein Service Replies to generate suggested responses for service agents. They want to ensure that agents always review and approve the suggested reply before it is sent to the customer. Which configuration best supports this requirement?

Accepted Answer

Configure Einstein Service Replies to require agent approval before sending. Einstein Service Replies can be configured to generate suggested responses for agents. To ensure agents review and approve before sending, you must enable the setting that requires agent approval. This is typically a configuration option in Service Cloud that prevents auto-send and forces the agent to edit or approve the reply. Auto-send would bypass review, toxicity detection is unrelated to approval, and using Einstein Bots is a different feature for automated conversations, not agent-assisted replies.

Answer

Use Einstein Bots instead of Service Replies

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Turn on toxicity detection for all replies

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Enable auto-send for Einstein Service Replies

Question 4

A marketing team wants to display personalized product recommendations on an Experience Cloud site. Which Einstein feature should they use?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it is specifically designed to deliver personalized product recommendations on Experience Cloud sites. It uses AI to analyze customer behavior and purchase history to suggest relevant products, directly meeting the marketing team's goal of displaying personalized product recommendations.

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Einstein Next Best Action

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Einstein Article Recommendations

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Einstein Prediction Builder

Question 5

A service manager wants to provide agents with suggested knowledge articles while they are working on a case. Which Einstein feature should be enabled?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it uses AI to analyze the case context (such as subject, description, and product) and suggests relevant knowledge articles directly within the Salesforce console while the agent works on the case. This matches the requirement of providing suggested knowledge articles during case handling.

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Einstein Case Classification

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Einstein Discovery

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Einstein Bots

Question 6

A company is designing an AI system to screen job applicants. To ensure fairness, which practice should be implemented?

Accepted Answer

Conduct regular fairness audits on model outcomes. Regular fairness audits are essential because they systematically evaluate model outcomes for bias across demographic groups, using metrics like disparate impact or equal opportunity difference. This practice aligns with responsible AI frameworks (e.g., NIST AI Risk Management Framework) and helps detect subtle biases that may emerge from proxy variables or data drift, ensuring the screening process remains equitable over time.

Answer

Use only one data source for consistency

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Maximize the model's accuracy on historical hiring decisions

Answer

Remove all demographic data from the training set

Question 7

A service team trains an Einstein Bot on historical chat transcripts. After deployment, the bot frequently fails to understand customer intents. Which action is most likely to improve performance?

Accepted Answer

Add more diverse training phrases per intent. Adding more diverse training phrases per intent directly addresses the root cause of the bot's failure to understand customer intents: insufficient coverage of the varied ways customers express the same goal. Einstein Bot uses natural language understanding (NLU) models that rely on example phrases to learn intent patterns; increasing the diversity of these phrases improves the model's ability to generalize to unseen utterances, reducing misclassification.

Answer

Increase the confidence threshold to 90%

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Use a hierarchical intent structure

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Reduce the number of intents to two

Question 8

A sales rep noticed that the Einstein Lead Scoring prediction bar shows 'No score available' for many leads. The admin confirmed that Einstein Lead Scoring is enabled and the permission set is assigned. What is the most likely cause?

Accepted Answer

The org does not have enough historical data to train the scoring model.. Einstein Lead Scoring requires a minimum amount of historical lead data (typically at least 2,000 converted and 2,000 unconverted leads) to train its predictive model. If the org lacks sufficient historical data, the model cannot generate scores, resulting in 'No score available' for leads. This is the most likely cause because the admin confirmed the feature and permissions are correctly enabled.

Answer

The leads have not yet been assigned to a user.

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The leads were created less than 30 days ago.

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The lead scoring model is still training.

Question 9

A customer service agent wants to receive suggested knowledge articles while working on a case. Which Einstein feature should be enabled?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it specifically uses AI to suggest relevant knowledge articles to a service agent based on the context of the case they are working on. This directly matches the requirement of receiving suggested knowledge articles while handling a case.

Answer

Einstein Case Classification

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Einstein GPT Service GPT

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Einstein Next Best Action

Question 10

An admin configures Einstein Lead Scoring but notices that scores for all leads are stuck at 99, even for clearly low-quality leads. What is the most likely cause?

Accepted Answer

The scoring model is not yet built or activated. Einstein Lead Scoring requires the scoring model to be built and activated before it can assign scores. If the model is not yet built or activated, the system defaults to a placeholder score of 99 for all leads, regardless of their actual quality. This explains why even low-quality leads show a score of 99.

Answer

All leads are from a high-quality source

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The lead score field is a formula field

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The lead score field is not added to the page layout