AI Associate Salesforce Einstein AI Features Practice Test 3 — 15 Questions

Question 1

A sales manager wants to automatically track emails and events from their sales team's Gmail accounts into Salesforce without manual logging. Which Salesforce feature should they enable?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (B) is the correct feature because it automatically syncs emails and events from Gmail (and Microsoft 365) into Salesforce without requiring manual logging by users. It uses a background synchronization process that captures activities based on configured rules, eliminating the need for manual entry or third-party integrations.

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Einstein Lead Scoring

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Einstein Email Insights

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Einstein Conversation Insights

Question 2

A company wants to predict which leads are most likely to convert. They have historical lead data with a 'Converted' field (True/False). Which Einstein feature should they use to build a custom prediction model from this data?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct choice because it allows users to build custom prediction models using their own historical data, including a binary outcome field like 'Converted'. It is designed for non-data scientists to create models without code, directly from standard or custom objects.

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Einstein Lead Scoring

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Einstein Discovery

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Einstein Opportunity Scoring

Question 3

A support manager wants to automatically suggest relevant knowledge articles when agents open a case. Which Einstein feature should they enable?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations automatically suggests relevant knowledge articles to agents based on the case details, helping resolve cases faster.

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Einstein Recommendation Builder

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Einstein Next Best Action

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Einstein Case Classification

Question 4

A company uses Einstein Forecasting. Their sales reps' committed forecasts are consistently lower than the AI-predicted forecast. The manager wants to understand why. What is the BEST first step to investigate the discrepancy?

Accepted Answer

Review the AI forecast explanation to see which factors (e.g., deal stage, historical win rates) are driving the higher prediction.. Einstein Forecasting provides an AI-predicted forecast based on historical data and deals. Comparing the rep commit to the AI forecast, and analyzing the key drivers behind the AI prediction (such as deal stage, age, amount) helps identify why the AI expects more.

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Disable Einstein Forecasting and revert to manager rollups only.

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Adjust the historical date range in forecast settings to exclude past low-performing quarters.

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Run a report on closed won opportunities to see if the AI overestimates.

Question 5

A service team uses Einstein Case Classification to auto-classify incoming cases. They notice that most cases are being classified as 'Low' priority regardless of the actual urgency. What is the most likely cause?

Accepted Answer

The historical case data used for training has a severe class imbalance with very few high-priority cases.. D is correct because a severe class imbalance in the historical training data—where high-priority cases are rare—causes the Einstein Case Classification model to bias predictions toward the majority class ('Low'). The model learns that most cases in the training set are low priority, so it classifies new cases as 'Low' even when the actual urgency is higher, as the algorithm optimizes for overall accuracy rather than per-class performance.

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The model is overfitting to the training data.

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The model has not been retrained in over 90 days.

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The field mapping for priority is incorrect in the classification configuration.

Question 6

A marketing manager wants to recommend products to visitors on a community site based on their browsing behavior. Which Salesforce feature is designed for this use case?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct feature because it is specifically designed to generate personalized product or content recommendations based on user behavior, such as browsing history and past interactions on a community site. It uses collaborative filtering and deep learning models to surface relevant items, matching the marketing manager's goal of recommending products to visitors.

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Einstein Prediction Builder

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Einstein Next Best Action

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Einstein Article Recommendations

Question 7

A developer wants to use AI to automatically extract key entities like dates, product names, and amounts from customer emails and store them in Salesforce fields. Which Einstein API should they use?

Accepted Answer

Einstein Vision and Language Platform API. Einstein Vision and Language Platform (Einstein Platform Services) provides APIs for text classification, named entity recognition (NER), and other AI tasks. NER is specifically for extracting entities like dates, product names, and amounts.

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Einstein Discovery API

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Einstein Prediction Builder API

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Einstein Bots API

Question 8

A company wants to build a custom AI prediction for churn rate using their Customer_Churn__c (Yes/No) field. In Einstein Prediction Builder, after selecting the object and prediction field, what is the next step?

Accepted Answer

Select the dataset (records to train on).. After selecting the prediction field (binary classification), you must select the dataset (records to train on) and then choose features (input fields) that the model will use to make predictions.

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Define the prediction score field and explanation field.

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Deploy the model to production.

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Select features (input fields) to train the model.

Question 9

A Salesforce admin is configuring Einstein Conversation Insights. They want to automatically capture next steps from sales call recordings. Which component of Conversation Insights provides this functionality?

Accepted Answer

Next step capture (powered by Einstein NLP). Option A is correct because Einstein Conversation Insights uses Einstein Natural Language Processing (NLP) to automatically analyze sales call transcripts and extract actionable next steps, such as follow-up tasks or commitments. This feature is specifically designed to identify and capture these items without manual effort, leveraging AI to parse conversational context.

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Call recording transcription

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Keyword tracking

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Talk-time metrics

Question 10

A sales rep wants to generate a personalized email to a prospect using AI. Which Einstein GPT feature should they use?

Accepted Answer

Sales GPT. Sales GPT includes email generation capabilities. It can create personalized email drafts based on CRM data and context.

Answer

Service GPT

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Prompt Builder

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Einstein Copilot

Question 11

A company uses Einstein Discovery to analyze their sales pipeline. They see a waterfall chart showing the expected revenue changes from one stage to the next. What does the waterfall chart primarily help identify?

Accepted Answer

The expected revenue at each stage and the changes between stages.. In Einstein Discovery, waterfall charts visualize the contribution of individual factors to a target metric (e.g., revenue). They show how each stage or factor adds or subtracts from the total, helping identify key drivers.

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The accuracy of the AI prediction compared to actuals.

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The improvement suggestions from Einstein Discovery.

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The top reasons why deals are won or lost.

Question 12

An administrator is setting up Einstein Bots for a service center. They want the bot to understand when a customer says 'I want to return my order' and route to the returns process. What must the admin configure in the bot builder?

Accepted Answer

Create a new intent called 'Return Order' and map it to the returns dialogue flow.. In Einstein Bots, intents represent the customer's goal (e.g., return order), and entities capture specifics (e.g., order number). The admin must define an intent for returns and train the NLP model with example phrases. Entities can capture order IDs.

Answer

Use the pre-built 'Returns' intent from the intent library.

Answer

Enable the 'Return Order' entity and add sample utterances directly to the dialogue.

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Add a keyword trigger for 'return' in the bot configuration.

Question 13

A company wants to use Agentforce to create an autonomous agent that can handle order cancellations. Which TWO components are required when building the agent in Agent Builder?

Accepted Answer

Actions. Actions (D) are required in Agent Builder because they define the specific tasks the agent can perform, such as invoking an Apex class, a Flow, or an external API to process an order cancellation. Topics (E) are required because they group related user intents and map them to the appropriate actions, enabling the agent to understand and route cancellation requests correctly.

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Flows

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Intents

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Prompts

Question 14

A data analyst is using Einstein Discovery to analyze customer churn. They want to understand the key drivers of churn and get actionable recommendations. Which THREE outputs does Einstein Discovery provide to meet this need?

Accepted Answer

Waterfall charts showing contribution of each variable. Waterfall charts in Einstein Discovery visually decompose the contribution of each predictor variable to the overall prediction, showing how much each driver increases or decreases the likelihood of churn. This directly helps the analyst identify the key drivers of churn, meeting the requirement to understand what factors are most influential.

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Prediction scores for each record

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A trained model for deployment

Question 15

A sales operations manager wants to use Einstein Opportunity Scoring to improve win rates. They want to view the opportunity score and understand why a particular score is high or low. Where can they see the score and explanation in Salesforce Lightning? (Choose TWO)

Accepted Answer

In the Opportunity list view as a column. Einstein Opportunity Scoring appears in the Opportunity record page as a score field and a component with explanation. It can also be added to list views and reports. The score field is automatically added to the Opportunity object.

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In Salesforce Mobile App under 'Today'

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In the Activity Timeline

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In the Einstein Discovery dashboard