AI Associate Salesforce Einstein AI Features Practice Test 5 — 15 Questions

Question 1

A company wants to build an autonomous AI agent that can handle customer inquiries end-to-end. Which TWO tools are part of Agentforce for building and testing agents?

Accepted Answer

Topics and Actions. Agent Builder (E) is the core Agentforce tool for creating autonomous agents, where you define an agent's role, instructions, and behavior in a conversational setup, making it essential for building agents. Topics and Actions (B) are the building blocks configured within Agent Builder that determine what an agent can talk about and what tasks it can perform, so they are integral to building and testing end-to-end agents. Einstein Bots (A) is a legacy chatbot tool that predates Agentforce and is not used to build autonomous agents. Prompt Builder (C) is a separate tool for creating reusable prompt templates for generative AI features, not for constructing agents. Einstein Copilot (D) is the earlier copilot experience that Agentforce evolved from, not the tool used to build and test agents.

Answer

Einstein Bots

Answer

Prompt Builder

Answer

Einstein Copilot

Question 2

An admin is configuring Einstein Prediction Builder to predict case escalation. Which TWO components must be selected during setup?

Accepted Answer

Prediction field (binary classification target). In Einstein Prediction Builder setup, the two required selections are the prediction field, which defines the binary classification target (e.g., whether a case will escalate), and the features, which are the input fields used to train the model. Option B is correct because the prediction field establishes the outcome variable the model learns to predict. Option C is correct because features are the input fields that provide the signals the model uses to make predictions. Options A, D, and E are not required selections in this step: the prediction explanation template is optional for explaining predictions, the data set is automatically derived from the object's records rather than manually selected, and the prediction score field name is generated or configured later, not chosen as a required setup component.

Answer

Prediction explanation template

Answer

Data set (records to train on)

Answer

Prediction score field name

Question 3

A sales team wants to automatically generate personalized follow-up emails after each meeting. Which Salesforce AI feature should they use?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it is specifically designed to generate personalized, AI-driven content like follow-up emails directly within Salesforce. It uses generative AI to create context-aware drafts based on meeting data, such as notes or summaries, without requiring custom prompts or additional configuration.

Answer

Prompt Builder

Answer

Einstein Activity Capture

Answer

Einstein Copilot

Question 4

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 5

Which Einstein feature uses strategy builder (flows, Apex) to recommend offers or actions to users at the right moment?

Accepted Answer

Einstein Next Best Action. Einstein Next Best Action is the correct answer because it is the Einstein feature that uses Strategy Builder (which includes flows and Apex) to define decision logic and recommend the most relevant offers or actions to users at the right moment. It evaluates real-time context and business rules to surface the optimal next step, such as a discount or a follow-up task, directly within the Salesforce user interface.

Answer

Einstein Copilot

Answer

Einstein Prediction Builder

Answer

Einstein Recommendation Builder

Question 6

A data scientist is using Einstein Vision and Language Platform for text classification. They need to handle custom entities (NER) and classify text into multiple categories. Which THREE capabilities of the Einstein Platform Services API should they use?

Accepted Answer

Sentiment Analysis. Option C (Sentiment Analysis) is correct because the Einstein Language Platform exposes a sentiment endpoint that returns a sentiment label and probability for a piece of text, which is a core text-classification capability the data scientist can call alongside their custom models. Option D (Text Classification) is correct because it is the exact Einstein Platform Services API capability for training and predicting on custom datasets with multiple categories, matching the requirement to classify text into multiple categories. Option E (Named Entity Recognition (NER)) is correct because the Einstein Language NER endpoint detects and extracts custom entities from text, which is precisely the custom-entity handling the data scientist needs. Options A (Object Detection) and B (Image Classification) do not belong because they are Einstein Vision capabilities that operate on images rather than text, so they cannot satisfy the text classification or NER requirements.

Answer

Object Detection

Answer

Image Classification

Question 7

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 dedicated Salesforce Einstein feature designed specifically to automatically prioritize leads based on their likelihood to convert. It uses predictive models that analyze historical lead data and engagement patterns to assign a score between 1 and 99, enabling sales teams to focus on high-conversion leads without manual effort.

Answer

Einstein Opportunity Scoring

Answer

Einstein Prediction Builder

Answer

Einstein Activity Capture

Question 8

A company needs to predict which support cases are likely to escalate based on historical case data. They have a clear binary outcome (escalated vs not escalated) and want to select features from their case records. Which Einstein tool should they use?

Accepted Answer

Einstein Prediction Builder. Einstein Prediction Builder is the correct tool because it enables users to create custom binary classification models using their own historical data without writing code. The requirement to predict a binary outcome (escalated vs not escalated) and select features from case records matches Prediction Builder's point-and-click interface for training a model on a custom object or standard object like Case.

Answer

Einstein Next Best Action

Answer

Einstein Discovery

Answer

Einstein Case Classification

Question 9

A sales rep wants to automatically generate a personalized email to a lead based on their recent activity. Which Einstein GPT feature should the rep use?

Accepted Answer

Sales GPT. Sales GPT is the correct feature because it is specifically designed for sales use cases, such as generating personalized emails to leads based on their recent activity. It leverages Einstein GPT to create tailored sales communications, unlike Service GPT which focuses on service interactions.

Answer

Service GPT

Answer

Prompt Builder

Answer

Einstein Copilot

Question 10

A sales manager wants to see an AI-generated prediction of how likely each opportunity is to close, alongside the sales rep's own forecast commit. Which feature should they use?

Accepted Answer

Einstein Forecasting. Einstein Forecasting is the correct feature because it directly combines AI-generated predictions (based on historical data and pipeline trends) with the sales rep's own forecast commit, allowing a side-by-side comparison. This enables sales managers to see both the predicted likelihood of closing and the human forecast in one view, which is exactly what the question describes.

Answer

Einstein Discovery

Answer

Einstein Opportunity Scoring

Answer

Einstein Prediction Builder

Question 11

A service team wants to use Einstein GPT to improve agent productivity. Which TWO Einstein GPT features are designed specifically for service scenarios?

Accepted Answer

Service GPT - Case Summaries. Service GPT - Case Summaries (B) is correct because it is a Service Cloud Einstein GPT feature that automatically generates concise summaries of a case's history, interactions, and key details, helping agents quickly understand context and reduce wrap-up time. Service GPT - Knowledge Article Drafts (C) is also correct because it is designed for service scenarios, using generative AI to draft knowledge articles from case data and resolutions, which speeds up knowledge creation and improves agent productivity. Einstein Lead Scoring (A) is a Sales Cloud feature that predicts which leads are most likely to convert, so it is not a service-specific Einstein GPT capability. Sales GPT - Email Generation (D) is a Sales Cloud feature for drafting sales emails, not a service scenario feature. Einstein Opportunity Scoring (E) is also a Sales Cloud feature that scores opportunities based on likelihood to close, so it does not belong to service scenarios.

Answer

Einstein Lead Scoring

Answer

Sales GPT - Email Generation

Answer

Einstein Opportunity Scoring

Question 12

A company is building an autonomous AI agent with Agentforce. They need to define what the agent can do and how it responds. Which THREE components must be configured in Agent Builder?

Accepted Answer

Topics. In Agentforce's Agent Builder, Topics (A) are required because they define the scope of the agent's responsibilities and the conversational areas it can handle, mapping user intent to the correct instructions and actions. Actions (C) are also required because they specify the concrete tasks the agent can perform, such as invoking Flows, Apex, or Prompt Templates, thereby determining what the agent can actually do. Testing in Agent Builder (E) is a required component because it provides the built-in testing interface where you validate the agent's topics, instructions, and actions before deployment, ensuring it responds correctly. Business outcomes (B) are not a configuration component in Agent Builder, and Prompt templates (D) are optional supporting assets that can be referenced by actions rather than a mandatory component themselves.

Answer

Business outcomes

Answer

Prompt templates

Question 13

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 14

An organization uses Einstein Lead Scoring and notices that leads with a score above 80 are being sent to the sales team too quickly, overwhelming them. The admin wants to adjust when leads are automatically assigned. What should the admin do?

Accepted Answer

Modify the lead assignment rule to only assign leads with scores above a higher threshold. Einstein Lead Scoring assigns a score (0–100) to each lead based on predictive models. The default assignment rule triggers when a lead's score exceeds a threshold (e.g., 80). To reduce the volume of leads sent to sales, the admin should raise that threshold in the lead assignment rule so only higher-scored leads are automatically assigned. This directly controls the flow without altering the scoring model itself.

Answer

Reduce the number of features used in scoring

Answer

Disable Einstein Lead Scoring and use a custom scoring model

Answer

Create a new lead queue and manually review all leads

Question 15

A company wants to use generative AI to assist sales reps with writing call summaries and follow-up emails. Which TWO Salesforce Einstein features can be used together to achieve this? (Choose 2)

Accepted Answer

Sales GPT. Sales GPT (B) is correct because it is the Sales Cloud Einstein generative AI feature that drafts sales-related content such as call summaries and follow-up emails directly within the sales rep workflow. Prompt Builder (D) is correct because it lets admins create reusable, customizable prompt templates grounded in Salesforce data, which can be invoked to generate the summaries and emails that Sales GPT-style experiences rely on. Together, Prompt Builder defines the prompt logic and data grounding while Sales GPT delivers the sales-focused generative output, matching the scenario's requirement. Einstein Prediction Builder (A) is a predictive scoring/classification tool, not a generative text feature, so it cannot write summaries or emails. Service GPT (C) is designed for customer service use cases like case replies, not sales rep call summaries and follow-ups. Einstein Copilot (E) is a conversational assistant, but it is not one of the two features specifically paired here for generating sales call summaries and follow-up emails.

Answer

Einstein Prediction Builder

Answer

Service GPT

Answer

Einstein Copilot