AI Associate Salesforce Einstein AI Features — 20 Questions

Question 1

A company uses Einstein Discovery to analyze sales data and wants to share the findings with business stakeholders who are not Salesforce users. What is the recommended way to share the story?

Accepted Answer

Download the story as a PDF and email it. Downloading the story as a PDF and emailing it is the recommended way to share Einstein Discovery findings with non-Salesforce users. It provides a static, easily distributable format that does not require login credentials.

Answer

Create a public Salesforce site to display the story

Answer

Grant the stakeholders a Salesforce login and viewer permission

Answer

Embed the story in a Chatter post

Question 2

A Salesforce admin needs to create a prompt template that generates a follow-up email after a meeting. Which Prompt Builder template type should be used?

Accepted Answer

Sales Email. The Sales Email template type in Prompt Builder is specifically designed for generating sales-related communications, such as follow-up emails after meetings. It includes pre-built fields and context (e.g., meeting notes, contact details) optimized for sales workflows, making it the correct choice for this use case.

Answer

Service Reply

Answer

Field Generation

Answer

Flex Prompt

Question 3

A company wants to use Einstein Conversation Insights to analyze call recordings. Which of the following metrics is NOT provided by this feature?

Accepted Answer

Next step capture. Einstein Conversation Insights provides talk time metrics, keyword tracking, and sentiment analysis. However, it does not provide next step capture, which is a separate Einstein feature. Therefore, the metric NOT provided is 'Next step capture'.

Answer

Keyword tracking

Answer

Talk time metrics

Answer

Sentiment analysis

Question 4

An admin needs to generate personalized sales email drafts for their team using generative AI. The emails should be based on context from the Salesforce record. Which feature should they use?

Accepted Answer

Sales GPT. Sales GPT is the Salesforce feature specifically designed to generate personalized sales email drafts grounded in CRM record context, such as opportunity, account, and contact data from the Salesforce record. It is purpose-built for the sales email drafting use case described.

Answer

Prompt Builder

Answer

Einstein Copilot

Answer

Einstein Next Best Action

Question 5

A company uses Einstein Conversation Insights to analyze sales call recordings. They want to automatically capture action items from each call. How can they achieve this?

Accepted Answer

Enable the 'Next Step Capture' feature in Conversation Insights settings. Einstein Conversation Insights includes a built-in 'Next Step Capture' feature that automatically identifies and extracts action items from sales call recordings. This feature uses natural language processing (NLP) to detect commitments, tasks, and follow-ups mentioned during the conversation, eliminating the need for manual transcription review or external integrations.

Answer

Use Einstein Call Summaries in Sales GPT

Answer

Manually create tasks from the call transcript

Answer

Integrate with Einstein Bots to capture steps

Question 6

A healthcare company uses Einstein Prediction Builder to predict patient no-shows. After training a model, they receive a low prediction accuracy. Which THREE actions should they take to improve?

Accepted Answer

Increase the number of records in the training dataset. Option A is correct because Einstein Prediction Builder learns from historical data, so increasing the number of records in the training dataset gives the model more examples to learn patterns from and generally improves prediction accuracy. Option C is correct because the prediction field defines the outcome the model is trying to predict; if the chosen field is poorly defined, inconsistent, or not truly predictive, changing it to a more appropriate field can substantially improve model performance. Option E is correct because adding more relevant input features gives the model additional signals and relationships to learn from, which improves its ability to distinguish no-show from show patients. Option B is not correct because Einstein Discovery is a separate analytics and prediction tool and is not the prescribed fix for improving an existing Prediction Builder model. Option D is not correct because Einstein Case Classification is used to predict and populate case fields, not to improve patient no-show prediction accuracy.

Answer

Use Einstein Discovery instead

Answer

Enable Einstein Case Classification

Question 7

A service manager wants to recommend knowledge articles to agents handling cases. Which TWO Einstein features can be used for this purpose?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations (D) is designed exactly for this scenario: it uses case data and machine learning to surface relevant knowledge articles to agents directly within the case record, helping them resolve cases faster. Einstein Next Best Action (E) can also recommend knowledge articles to agents by delivering targeted recommendations (including article suggestions) through a strategy built on business rules and predictive models, so it fits the goal of recommending articles during case handling. Einstein Bots (A) is a chatbot feature for automating conversations with customers, not for recommending articles to agents. Einstein Prediction Builder (B) creates custom predictions on any object/field but does not itself deliver article recommendations to agents. Einstein Case Classification (C) predicts field values such as priority or type on cases, not knowledge article suggestions.

Answer

Einstein Bots

Answer

Einstein Prediction Builder

Answer

Einstein Case Classification

Question 8

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.

Answer

Einstein Prediction Builder

Answer

Einstein Next Best Action

Answer

Einstein Article Recommendations

Question 9

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. The Einstein Vision and Language Platform API provides pre-built models for image and text analysis, including entity extraction from text. It can identify dates, product names, amounts, and other entities from customer emails.

Answer

Einstein Discovery API

Answer

Einstein Prediction Builder API

Answer

Einstein Bots API

Question 10

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). 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.

Answer

Call recording transcription

Answer

Keyword tracking

Answer

Talk-time metrics

Question 11

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 is the Einstein GPT feature purpose-built for sales use cases, including generating personalized prospect emails, drafting follow-ups, and summarizing opportunities within Sales Cloud. It is grounded in CRM data such as the prospect's record, activity history, and opportunity details, which is what makes the email personalized. Prompt Builder is a general prompt-template tool, Einstein Copilot is a conversational assistant, and Service GPT targets customer service.

Answer

Service GPT

Answer

Prompt Builder

Answer

Einstein Copilot

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, the admin must create a custom intent (e.g., 'Return Order') and attach training utterances, then map that intent to the dialogue flow that handles returns. Intents represent the customer's goal, and the bot's NLU engine matches incoming utterances to the intent with the highest confidence, triggering the mapped dialogue. This is the standard bot-builder workflow for a bespoke business process.

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.

Answer

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

Question 13

A sales rep wants to quickly generate a personalized email to a lead without leaving Salesforce. Which Einstein GPT feature should they use?

Accepted Answer

Sales GPT. Sales GPT is the correct Einstein GPT feature because it is specifically designed for sales use cases, such as generating personalized emails to leads directly within Salesforce. It leverages generative AI to create tailored content based on CRM data, enabling quick, context-aware communication without leaving the platform.

Answer

Einstein Copilot

Answer

Service GPT

Answer

Prompt Builder

Question 14

A company wants to build a custom AI prediction model that predicts whether a customer will churn (yes/no) based on Salesforce data. They have historical data on churned and retained customers. Which Einstein feature should they use, and what type of prediction field is required?

Accepted Answer

Einstein Prediction Builder, with a picklist prediction field containing 'Churned' and 'Not Churned'. Einstein Prediction Builder is the no-code tool for building custom binary classification models on Salesforce data, and it requires a picklist field with exactly two values as the prediction target. Since churn is a yes/no outcome, a picklist containing 'Churned' and 'Not Churned' is the correct prediction field type.

Answer

Einstein Lead Scoring, with a formula field

Answer

Einstein Discovery, with a numeric prediction field

Answer

Einstein Prediction Builder, with a checkbox prediction field

Question 15

A sales operations manager notices that Einstein Lead Scoring is not producing scores for some leads. The leads have all required fields populated. What is the most likely cause?

Accepted Answer

There are fewer than 500 leads with the score field populated. Einstein Lead Scoring requires a minimum of 500 leads with the score field populated to train and generate scores. If there are fewer than 500 such leads, the model cannot produce scores, even if all required fields are populated. This is a documented data threshold for the feature.

Answer

The user does not have the 'View Lead Score' permission

Answer

The lead source field is not included as a feature

Answer

The leads were created in a different Salesforce instance

Question 16

A company wants to use Einstein Forecasting to compare AI-generated predictions against sales rep commitments. They have enabled the feature and entered their sales data. What must they do to view the AI forecast?

Accepted Answer

Turn on Einstein AI in the Forecasts settings. After enabling Einstein Forecasting and entering sales data, the user must turn on Einstein AI in the Forecasts settings to activate the AI-generated predictions. This setting allows the system to compare AI forecasts against sales rep commitments within the standard Forecasts component.

Answer

Run Einstein Discovery on opportunity data

Answer

Enable Einstein Activity Capture

Answer

Create a custom report type for forecasts

Question 17

A company needs to analyze thousands of customer feedback comments to identify common themes and sentiment. They want to use a prebuilt Salesforce AI solution. Which approach is best?

Accepted Answer

Use Einstein Discovery to analyze the feedback data and identify themes. Einstein Discovery can perform automated statistical analysis on text data to identify themes and patterns, including sentiment analysis, without requiring custom model training.

Answer

Use Einstein Prediction Builder to predict sentiment

Answer

Use Einstein Vision and Language Platform to build a custom text classification model

Answer

Use Einstein Bots to collect more feedback

Question 18

A sales team wants to use Einstein Opportunity Scoring to improve win rates. Which TWO statements about Einstein Opportunity Scoring are correct? (Choose 2)

Accepted Answer

It provides a score from 1 to 99 indicating the likelihood of winning the opportunity. Option A is correct because Einstein Opportunity Scoring assigns each opportunity a score from 1 to 99 that represents the likelihood that the opportunity will be won, giving the sales team a quantitative way to prioritize deals. Option E is correct because the score, along with the factors and influences that contributed to it, is surfaced directly on the opportunity record page in Lightning Experience, so reps can see why the score is what it is. Options B, C, and D are not correct: the minimum data requirement for Opportunity Scoring is 200 closed-won and 200 closed-lost opportunities (not 500 closed opportunities in 12 months), the score is not exposed through an out-of-the-box REST API endpoint, and Einstein Opportunity Scoring does not automatically change the opportunity stage—it only predicts the likelihood of winning.

Answer

It requires a minimum of 500 closed opportunities in the last 12 months to activate

Answer

The score can be accessed via the REST API out-of-the-box

Answer

It automatically updates the opportunity stage based on the score

Question 19

A support center wants to use Einstein Case Classification to automatically assign categories to incoming cases. They have historical case data with the 'Type' field populated for 70% of cases, 'Priority' for 50%, and 'Reason' for 30%. They want to classify on 'Type' and 'Reason'. What is the best approach to maximize model accuracy?

Accepted Answer

Start by training a model for Type only, since it has more populated records, then train a model for Reason once more data is accumulated.. Einstein Case Classification requires a minimum volume of labeled training records per field to build a reliable model, and Reason at 30% populated likely falls below the threshold for acceptable accuracy. Training Type first (70% populated) delivers immediate value, and deferring Reason until more data accumulates avoids a low-quality model. This staged approach maximizes overall accuracy and ROI.

Answer

Use Einstein Prediction Builder instead, which can handle multi-class classification for both fields.

Answer

Build a single model that predicts both Type and Reason simultaneously to leverage all available data.

Answer

Create two separate models: one for Type using all records with Type populated, and one for Reason using all records with Reason populated.

Question 20

A company wants to automatically generate draft email replies to customer support inquiries using generative AI. Which Einstein GPT feature is designed for this purpose?

Accepted Answer

Service GPT. Service GPT is specifically designed to assist customer service agents by generating draft email replies, summarizing cases, and suggesting responses. It leverages generative AI to automate support interactions.

Answer

Einstein Copilot

Answer

Sales GPT

Answer

Einstein Discovery