AI Associate Practice Test 22 — 15 Questions

Question 1

A service manager wants to automatically assign the correct 'Type' and 'Priority' fields on incoming cases. Which feature automatically classifies cases using AI?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it uses AI to automatically predict and assign the 'Type' and 'Priority' fields on incoming cases based on historical case data and patterns. It leverages machine learning models trained on past cases to classify new cases without requiring manual rules or human intervention, directly meeting the service manager's requirement.

Answer

Einstein Article Recommendations

Answer

Einstein Prediction Builder

Answer

Einstein Bots

Question 2

Which THREE factors should be considered when selecting features for a predictive model in Salesforce?

Accepted Answer

Correlation between features to avoid multicollinearity. Multicollinearity occurs when two or more features are highly correlated, which can destabilize model coefficients and reduce interpretability. In Salesforce's predictive models, such as those built with Einstein Discovery, correlated features can inflate variance and lead to unreliable predictions. Avoiding multicollinearity ensures that the model's feature importance estimates are trustworthy and that the model generalizes well to new data.

Answer

Volume of data available for each feature

Answer

Compliance with data privacy regulations

Question 3

A call center manager wants to analyze recorded sales calls to identify keywords, measure talk-time, and capture follow-up tasks. Which Einstein feature provides these capabilities?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct answer because it is specifically designed to analyze recorded sales calls by transcribing conversations, identifying keywords and topics, measuring talk-time, and capturing follow-up tasks. It uses natural language processing (NLP) to extract actionable insights from voice and text interactions, directly matching the requirements in the question.

Answer

Einstein Discovery

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Einstein Activity Capture

Answer

Einstein Email Insights

Question 4

A sales manager wants to use Einstein Opportunity Scoring to improve forecasting. Which TWO statements are true about Einstein Opportunity Scoring?

Accepted Answer

Score factors are displayed in the Lightning opportunity record.. Einstein Opportunity Scoring automatically surfaces the key factors influencing the predicted score directly on the Lightning opportunity record. This allows sales reps to see which attributes (e.g., deal size, industry, engagement) are driving the win likelihood, enabling them to take targeted actions to improve the forecast.

Answer

It can be used to automatically update opportunity stage.

Answer

It compares the AI-predicted score to the rep's commit amount.

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It requires the admin to build a custom prediction model.

Question 5

A business analyst wants to create a custom AI model that predicts whether a lead will convert, based on historical lead data. They need to select the correct prediction field, data set, and features. Which Salesforce tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it allows a business analyst to create a custom AI model that predicts a specific outcome (e.g., lead conversion) using their own historical data and selected features. Unlike pre-built scoring models, Prediction Builder enables custom prediction field selection, dataset upload, and feature engineering without requiring data science expertise.

Answer

Einstein Lead Scoring

Answer

Einstein Opportunity Scoring

Answer

Einstein Discovery

Question 6

A sales rep wants Einstein GPT to generate a personalized email to a prospect based on recent account activity. Which Salesforce GPT feature should the rep use?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it is specifically designed to generate personalized sales emails based on recent account activity, leveraging CRM data and generative AI to create context-aware outreach. Einstein Copilot is a conversational assistant, not a dedicated email generation tool, while Prompt Builder requires manual prompt creation and lacks the automated, activity-triggered personalization that Sales GPT provides. Service GPT focuses on service-related use cases like case summaries and replies, not sales prospecting.

Answer

Einstein Copilot

Answer

Prompt Builder

Answer

Service GPT

Question 7

A sales operations manager wants to use Einstein Forecasting to improve forecast accuracy. Which TWO capabilities does Einstein Forecasting provide beyond traditional manager rollups? (Select two.)

Accepted Answer

Comparison of AI forecast to the rep's commit amount. Einstein Forecasting provides a direct comparison between the AI-generated forecast and the sales rep's manually entered commit amount. This allows managers to see where human judgment and AI predictions diverge, enabling data-driven coaching and more accurate forecasting. Traditional manager rollups only aggregate rep commits without this AI-based validation layer.

Answer

Automated opportunity scoring for each deal

Answer

Generation of call scripts for sales reps

Answer

Automatic adjustment of quota targets based on AI predictions

Question 8

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

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Question 9

A company building an Einstein Bot for customer support wants to ensure that when the bot cannot resolve an issue, the conversation is seamlessly transferred to a human agent. Which THREE steps are required to enable this handoff? (Select three.)

Accepted Answer

Assign the bot to a Service Cloud user who can accept chat transfers. Assigning the bot to a Service Cloud user who can accept chat transfers is correct because the Einstein Bot must be linked to a Service Cloud user with the appropriate permissions and presence status to receive and handle transferred conversations. This ensures the user is available in the Omni-Channel routing system and can accept the chat when the bot escalates.

Answer

Create a custom field on the case object to store escalation reason

Answer

Enable Einstein GPT for Bots to generate handoff scripts

Question 10

A service manager wants to analyze historical case data to identify the most common reasons for escalations and get actionable suggestions to reduce them. Which Einstein tool should they use?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct tool because it is designed to analyze historical data, identify patterns, and provide actionable recommendations to improve business outcomes. In this scenario, it can analyze past case escalation data to uncover root causes and suggest specific actions to reduce escalations, which aligns directly with the service manager's goal.

Answer

Einstein Prediction Builder

Answer

Einstein Case Classification

Answer

Einstein Conversation Insights

Question 11

A service agent is working on a case and needs to quickly find relevant knowledge articles without searching manually. Which Einstein feature can automatically suggest articles based on the case details?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it automatically suggests relevant knowledge articles based on the case details, such as subject, description, and product. This eliminates the need for manual search by leveraging AI to match case context with article content.

Answer

Einstein Next Best Action

Answer

Einstein Search

Answer

Einstein Case Classification

Question 12

A company wants to build a custom AI model that predicts whether a customer will churn within the next 30 days, using data from multiple Salesforce objects. The prediction should output a score from 0 to 100. Which Einstein feature is most appropriate?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct choice because it allows users to build custom predictive models using data from multiple Salesforce objects without writing code, and it outputs a score (0–100) representing the likelihood of a specific outcome, such as customer churn within 30 days. This feature is designed for point-and-click creation of binary classification models that generate a probability score, directly matching the requirement.

Answer

Einstein Discovery

Answer

Einstein Case Classification

Answer

Einstein Next Best Action

Question 13

A company implements Einstein Opportunity Scoring and wants to understand where the opportunity scores appear in the Salesforce Lightning interface. Which two locations display the score?

Accepted Answer

List view column. Einstein Opportunity Scoring can be added as a column in list views, allowing users to see the score for each opportunity directly in the list. Option D is correct because the score is also displayed on the opportunity record page via a dedicated component, providing detailed scoring insights at the record level.

Answer

Global search results

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Home page sidebar

Answer

Report chart

Question 14

A large enterprise is using Einstein Lead Scoring and notices that the model score is not updating for leads created via a web-to-lead form. The leads have all required fields populated. The admin has verified that the model is active and the data source includes the Lead object. What could be causing the score to remain static?

Accepted Answer

The model has not scored enough leads to start scoring new ones. Einstein Lead Scoring requires a minimum number of scored leads (typically 500) before it begins scoring new leads. Until that threshold is met, the model remains in a 'training' or 'pending' state and will not update scores for any leads, including those from web-to-lead forms. The admin has confirmed the model is active and the Lead object is included, so the most likely cause is that the model has not yet processed enough leads to start scoring.

Answer

The data source excludes leads created by web-to-lead

Answer

Web-to-lead leads are not supported by Einstein AI

Answer

The model is not yet activated

Question 15

A sales manager wants to see an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction. Which feature provides this capability directly in the opportunity record?

Accepted Answer

Einstein Opportunity Scoring. Einstein Opportunity Scoring is the correct feature because it directly provides an AI-generated prediction of which opportunities are most likely to close, along with the key factors influencing that prediction, all displayed within the opportunity record. This feature uses machine learning models to analyze historical data and assign a score (0–100) to each opportunity, surfacing the top positive and negative influencing factors to help sales reps prioritize their efforts.

Answer

Einstein Forecasting

Answer

Einstein Activity Capture

Answer

Einstein Lead Scoring