AI Associate Salesforce Einstein AI Features Practice Test 18 — 15 Questions

Question 1

A marketing team wants to recommend relevant knowledge articles to service agents while they work on a case. Which Einstein feature provides these recommendations?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations is the correct feature because it specifically uses AI to analyze the context of a service case—such as case subject, description, and product—and then surfaces the most relevant knowledge articles directly within the Salesforce console for the agent. This is distinct from general recommendation engines because it is purpose-built for knowledge article suggestions in service workflows.

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Einstein Next Best Action

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Einstein Recommendation Builder

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Einstein Case Classification

Question 2

Which Einstein feature provides AI-enhanced forecast predictions that go beyond manager rollups and can compare AI forecast to rep commitments?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct answer because it uses AI to generate predictive forecasts that go beyond traditional manager rollups, and it allows users to compare the AI-generated forecast against individual rep commitments. This feature leverages historical data and patterns to provide more accurate predictions, enabling sales leaders to identify discrepancies between AI insights and human inputs.

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Einstein Opportunity Scoring

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Einstein Discovery

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Einstein Lead Scoring

Question 3

A company wants to offer personalized product recommendations on their Experience Cloud site. Which Einstein feature should be used?

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 user behavior and preferences to suggest relevant products, directly matching the requirement for personalized product recommendations.

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Einstein Prediction Builder

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Einstein Article Recommendations

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Einstein Next Best Action

Question 4

A developer wants to use AI to classify images uploaded to Salesforce. Which platform/service should they use?

Accepted Answer

Einstein Vision and Language Platform. Einstein Vision and Language Platform is the correct service because it provides pre-built and custom AI models specifically designed for image classification, object detection, and optical character recognition (OCR) within Salesforce. It allows developers to upload images and train models using labeled data directly in the Salesforce ecosystem, making it the only option that directly addresses the requirement to classify images.

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Einstein Bots

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Einstein GPT

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Einstein Discovery

Question 5

A sales operations manager wants to use Einstein GPT for Sales to improve rep productivity. Which THREE tasks can Sales GPT perform?

Accepted Answer

Summarize sales calls. Einstein GPT for Sales includes a call summarization feature that uses generative AI to automatically create concise summaries of sales calls from transcripts. This directly improves rep productivity by saving time on manual note-taking and capturing key action items.

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Create sales dashboards

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Predict lead scores

Question 6

Which feature uses automated statistical analysis to identify key drivers in your data and generate natural language stories about the insights?

Accepted Answer

Einstein Discovery. Einstein Discovery is the correct answer because it is the Salesforce feature specifically designed to perform automated statistical analysis (using regression, classification, and clustering algorithms) on your data to identify key drivers and patterns, then automatically generate natural language narratives that explain those insights in plain English. This combines machine learning with automated insight communication, which directly matches the question's description.

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Einstein Prediction Builder

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Einstein Conversation Insights

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Einstein Next Best Action

Question 7

A company uses Einstein Lead Scoring and notices that leads with a score above 90 are not converting as expected. They suspect the model is overfit to historical patterns. What should they do to improve model performance?

Accepted Answer

Retrain the model by including more recent leads and removing outdated ones. Overfitting occurs when a model learns historical patterns that are no longer relevant. By retraining the model with more recent leads and removing outdated ones, the model can adapt to current conversion behaviors and reduce overfitting, improving predictive performance.

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Increase the score range from 1-99 to 1-100

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Manually adjust lead scores for high-scoring leads

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Add more features to the model to capture more signals

Question 8

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

Accepted Answer

Einstein Case Classification. Einstein Case Classification is the correct feature because it is specifically designed to automatically predict and populate standard case fields—such as Type, Priority, and Reason—by analyzing the unstructured text in the case description using natural language processing (NLP) and machine learning models. This eliminates manual data entry and ensures consistent categorization across incoming cases.

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Einstein Recommendation Builder

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Einstein Next Best Action

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Einstein Article Recommendations

Question 9

A developer is building an autonomous AI agent with Agentforce. They need the agent to perform actions in Salesforce, such as updating records and sending emails. How should they define these capabilities in Agent Builder?

Accepted Answer

Create actions in Agent Builder, specifying the operation and parameters. In Agent Builder, actions are the mechanism that defines what an autonomous AI agent can do in Salesforce, such as updating records or sending emails. Each action specifies the operation (e.g., a standard or custom action) and its parameters, allowing the agent to execute precise tasks without additional coding.

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Define topics that correspond to each action

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Use Prompt Builder to create prompts for each action

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Write Apex triggers to handle agent requests

Question 10

A company wants to use Einstein to forecast sales beyond simple manager rollups, comparing AI predictions with rep commitments. Which feature should they enable?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it is specifically designed to generate AI-driven sales forecasts by analyzing historical data, pipeline trends, and external factors, then comparing those predictions against rep commitments. Unlike simple manager rollups, it provides a statistical baseline that helps identify gaps between AI predictions and human estimates, enabling more accurate revenue planning.

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Einstein Discovery

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Einstein Prediction Builder

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Einstein Opportunity Scoring

Question 11

A service agent needs quick access to relevant knowledge articles while handling a case. Which Einstein feature can suggest the most relevant articles automatically?

Accepted Answer

Einstein Article Recommendations. Einstein Article Recommendations (C) is the correct feature because it uses AI to automatically surface the most relevant knowledge articles based on the context of the case, such as subject, description, and product. This directly addresses the need for quick access to relevant articles without manual search, leveraging Salesforce's predictive AI to match case data with article content.

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Einstein Next Best Action

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Einstein Case Classification

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Einstein Service GPT

Question 12

Which Einstein feature records call recordings and provides analysis on keywords, talk-time metrics, and next steps?

Accepted Answer

Einstein Conversation Insights. Einstein Conversation Insights is the correct feature because it is specifically designed to record and analyze sales calls, providing transcriptions, keyword spotting, talk-time metrics, and automated next steps. It uses natural language processing (NLP) to surface insights from conversational data, directly matching the question's requirements.

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Einstein Email Insights

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Einstein Activity Capture

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Einstein Discovery

Question 13

An organization wants to use Einstein GPT to generate case summaries. However, they need to ensure that the generated text adheres to company style and includes specific required fields. Which tool should they use to customize the prompts?

Accepted Answer

Prompt Builder. Prompt Builder is the correct tool because it allows administrators to create and manage custom prompt templates that enforce company style and required fields when generating content with Einstein GPT. Unlike other options, Prompt Builder is specifically designed to tailor generative AI outputs by defining instructions, variables, and guardrails for use cases like case summaries.

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Einstein Copilot

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Einstein Next Best Action

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Einstein Service GPT

Question 14

A retail company wants to display personalized product recommendations on their Experience Cloud site based on customer browsing behavior. Which Einstein feature should they implement?

Accepted Answer

Einstein Recommendation Builder. Einstein Recommendation Builder is the correct choice because it is specifically designed to deliver personalized product recommendations on Experience Cloud sites by analyzing customer browsing behavior, purchase history, and other engagement signals. It uses AI to surface the most relevant products in real time, directly matching the requirement for personalized product recommendations based on browsing behavior.

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Einstein Next Best Action

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Einstein Prediction Builder

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Einstein Article Recommendations

Question 15

A company wants to use Einstein Bots to handle customer inquiries. They need to train the bot to understand different customer intents. Which TWO components are essential for defining bot understanding?

Accepted Answer

Intents. Intents (A) are essential because they represent the customer's goal or purpose behind a message (e.g., "Check Order Status" or "Reset Password"), and Einstein Bots use training phrases tied to each intent to classify incoming utterances via natural language understanding. Entities (B) are also essential because they extract specific variable data from within an utterance—such as order numbers, dates, or product names—that the bot needs to fulfill the matched intent. Together, intents and entities form the core NLU model that defines how the bot understands what the customer wants and the key details involved. Actions (C) are not part of defining understanding; they define what the bot does after understanding. Topics (D) and Dialogue Flows (E) govern conversation routing and the sequence of dialog steps, not the intent/entity understanding model itself.

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Actions

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Topics

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Dialogue flows