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

Question 1

A company wants to build a chatbot for their customer portal that can handle returns and refunds. They want the bot to understand phrases like 'I want to return my order' or 'refund request'. What must they configure in Einstein Bots to recognize these variations?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Define an intent named 'Return_Refund' and add training phrases like those examples. Intents represent the purpose of the user's input, and entities capture key details like order numbers. The bot uses NLP to match phrases to intents. Dialogues define the flow, and analytics track performance.

Answer

Enable the Einstein Bots API for external integrations

Answer

Create a dialogue flow that asks clarifying questions

Answer

Set up bot analytics to monitor user utterances

Question 2

An admin is troubleshooting why Einstein Lead Scoring is not generating scores for some leads. They have enabled the feature and assigned permission sets. Which two factors could cause scores to be missing?

Accepted Answer

The lead's status is 'Converted' and scoring is configured to exclude converted leads. Einstein Lead Scoring can be configured to exclude leads with a 'Converted' status. If the scoring model is set to skip converted leads, those leads will not receive a score, even if the feature is enabled and permission sets are assigned. This is a common configuration setting that directly prevents scoring for converted records.

Answer

The lead has a custom field that is not included in the model

Answer

The lead does not have an email address

Answer

The lead's owner does not have a Salesforce license

Question 3

A sales manager wants to automatically surface the most important emails that require immediate attention from a high volume of daily customer messages. Which Einstein feature should they enable?

Accepted Answer

Einstein Email Insights. Einstein Email Insights (D) is the correct feature because it uses natural language processing (NLP) to analyze email content and metadata, automatically prioritizing messages that require immediate attention based on urgency, sender importance, and context. This directly addresses the sales manager's need to surface critical emails from a high volume of daily customer messages without manual sorting.

Answer

Einstein Conversation Insights

Answer

Einstein Activity Capture

Answer

Einstein Lead Scoring

Question 4

A company uses Einstein Prediction Builder to predict which leads will convert. They have a binary outcome field 'Converted__c' which is true for 8% of leads. After training, the model shows high accuracy (95%) but very low precision for the positive class. What is the most likely cause?

Accepted Answer

The data is imbalanced favoring the negative class. The dataset is imbalanced: only 8% of leads are positive (Converted__c = true), while 92% are negative. In such a scenario, a model can achieve 95% accuracy by simply predicting the majority class (negative) for all leads, but this yields very low precision for the positive class because it rarely predicts positive correctly. Einstein Prediction Builder, like most ML models, is sensitive to class imbalance, and without techniques like oversampling or threshold tuning, the model will favor the majority class.

Answer

The prediction field is not a binary field

Answer

The prediction score field is not configured correctly

Answer

The dataset is too small for training

Question 5

A service manager wants to automatically categorize incoming support cases into appropriate Type, Priority, and Reason fields based on the case description. Which Einstein feature should they use?

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate case fields such as Type, Priority, and Reason based on the case description. It uses natural language processing (NLP) to analyze the text and map it to predefined picklist values, enabling automated categorization without manual rules.

Answer

Einstein Prediction Builder

Answer

Einstein Article Recommendations

Answer

Einstein Next Best Action

Question 6

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

Answer

The lead score field is a formula field

Answer

The lead score field is not added to the page layout

Question 7

A company wants to use generative AI to draft email replies to common customer inquiries in Service Cloud. The replies should be based on company-approved templates and knowledge articles. Which feature should they use?

Accepted Answer

Service GPT. Service GPT includes reply recommendations that generate drafts based on knowledge articles and approved templates, helping agents respond quickly and consistently.

Answer

Einstein Recommendation Builder

Answer

Einstein Copilot

Answer

Sales GPT

Question 8

Which Einstein feature uses AI to automatically log user emails and events to Salesforce records without manual effort?

Accepted Answer

Einstein Activity Capture. Einstein Activity Capture (D) is the correct answer because it is the specific Einstein feature that automatically logs user emails and events to Salesforce records without manual effort. It uses AI to analyze email and calendar data from connected systems (like Gmail or Outlook) and automatically associates them with the relevant Salesforce records, eliminating the need for manual logging.

Answer

Einstein Conversation Insights

Answer

Einstein Email Insights

Answer

Einstein Lead Scoring

Question 9

A sales team wants to compare their own forecast amounts with an AI-generated prediction based on historical data and trends. Which Salesforce feature provides this comparison?

Accepted Answer

Einstein Forecasting. Einstein Forecasting provides AI-generated predictions that can be compared against reps' commit forecasts, helping identify risks and opportunities.

Answer

Einstein Opportunity Scoring

Answer

Einstein Lead Scoring

Answer

Einstein Prediction Builder

Question 10

An admin wants to create a prompt template that generates a personalized sales email for a lead using fields like Company, Industry, and Lead Source. The email should be generated when a user clicks a button on the lead record. Which Prompt Builder template type is appropriate?

Accepted Answer

Sales Email. The Sales Email template type in Prompt Builder is specifically designed to generate personalized sales emails using standard fields like Company, Industry, and Lead Source, and it triggers on a button click on the lead record. This template type includes built-in placeholders and formatting optimized for email generation, making it the correct choice for this use case.

Answer

Flex Prompt

Answer

Case Field Generation

Answer

Field Generation

Question 11

A sales rep wants to quickly generate a summary of a phone call recorded in Salesforce for their records. Which Einstein feature can automatically generate call summaries from recorded conversations?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it uses natural language processing (NLP) to automatically analyze recorded phone calls and generate concise summaries, key topics, and action items. This allows sales reps to quickly capture call outcomes without manual note-taking, directly within Salesforce.

Answer

Sales GPT

Answer

Einstein Activity Capture

Answer

Einstein Email Insights

Question 12

A company wants to build an autonomous AI agent with Agentforce that can handle customer inquiries about order status and returns. The agent should escalate to a human agent when it cannot resolve the issue. Which two components must be configured in Agent Builder?

Accepted Answer

Topics. Topics are correct because they define the specific areas of customer inquiries (e.g., order status, returns) that the autonomous agent can handle. In Agentforce, Topics act as the primary organizational unit that groups related intents and actions, enabling the agent to route conversations appropriately. Without Topics, the agent would lack the structured domain knowledge needed to process and escalate customer issues.

Answer

Intents

Answer

Entities

Answer

Testing

Question 13

A data analyst uses Einstein Discovery to analyze a dataset and receives a story that includes a waterfall chart and improvement suggestions. The analyst wants to share the insights with business users who don't have access to Einstein Discovery. Which three methods can they use to share the results?

Accepted Answer

Export the story as a PDF. Einstein Discovery allows users to export a story as a PDF, which can then be shared with business users who lack direct access to Einstein Discovery. This method provides a static, portable snapshot of the insights, including the waterfall chart and improvement suggestions, without requiring the recipients to have any Salesforce or Einstein licenses.

Answer

Send an email with the raw data

Answer

Create a custom mobile app

Question 14

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

Answer

Home page sidebar

Answer

Report chart

Question 15

A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Salesforce Einstein feature should be used?

Accepted Answer

Einstein Lead Scoring. Einstein Lead Scoring is the correct feature because it is specifically designed to automatically prioritize leads based on their likelihood to convert, using historical data and machine learning models. It assigns a score (0–100) to each lead, enabling sales teams to focus on high-conversion leads without manual intervention.

Answer

Einstein Opportunity Scoring

Answer

Einstein Prediction Builder

Answer

Einstein Discovery