Salesforce Salesforce Agentforce Specialist Practice Exams
Last updated on Oct 22,2026- Exam Code: Salesforce Agentforce Specialist
- Exam Name: Salesforce Agentforce Specialist
- Certification Provider: Salesforce
- Latest update: Oct 22,2026
Question #161
Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. The goal is to enhance the team’s performance by identifying areas for improvement and competitive intelligence.
Which feature provides insights about competitor mentions and coaching opportunities?
- A . Call Summaries
- B . Einstein Sales Insights
- C . Call Explorer
Correct Answer: C
C
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
Reference: Salesforce Einstein Conversation Insights Documentation: https://help.salesforce.com/s/articleView?id=einstein_conversation_insights.htm
C
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
Reference: Salesforce Einstein Conversation Insights Documentation: https://help.salesforce.com/s/articleView?id=einstein_conversation_insights.htm
Question #162
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?
- A . Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.
- B . Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.
- C . Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let’s analyze their roles.
Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.
In a Knowledge-based data library, identifying fields (e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context. Content fields (e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce’s documentation on Knowledge integration, making it the correct answer.
Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.
Identifying fields do more than categorize―they actively locate articles, not just index them. Content
fields aren’t limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.
While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:
The primary difference―identifying fields for locating articles and content fields for enriching responses―reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
Reference: Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup C Defines identifying vs. content fields.
Trailhead: Ground Your Agentforce Prompts C Explains field roles in Knowledge integration.
Salesforce Help: Knowledge in Agentforce C Confirms locating and enriching functions.
A
Explanation:
Comprehensive and Detailed In-Depth
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let’s analyze their roles.
Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.
In a Knowledge-based data library, identifying fields (e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context. Content fields (e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce’s documentation on Knowledge integration, making it the correct answer.
Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.
Identifying fields do more than categorize―they actively locate articles, not just index them. Content
fields aren’t limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.
While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:
The primary difference―identifying fields for locating articles and content fields for enriching responses―reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
Reference: Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup C Defines identifying vs. content fields.
Trailhead: Ground Your Agentforce Prompts C Explains field roles in Knowledge integration.
Salesforce Help: Knowledge in Agentforce C Confirms locating and enriching functions.
Question #163
Which object stores the conversation transcript between the customer and the agent?
- A . Messaging End User
- B . Messaging Session
- C . Case
Correct Answer: B
B
Explanation:
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.
Key Features of the Messaging Session Object:
Stores the Entire Customer-Agent Conversation
The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.
o This ensures that past interactions can be referenced during follow-ups.
Supports AI-Powered Work Summaries
Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
oThese summaries are stored and accessible for later reference.
Links with Service Cloud for Case Resolution
If a conversation escalates into a case, the Messaging Session object can be linked to it.
oThis allows support teams to review the conversation history without switching contexts.
Why Not the Other Options?
B
Explanation:
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.
Key Features of the Messaging Session Object:
Stores the Entire Customer-Agent Conversation
The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.
o This ensures that past interactions can be referenced during follow-ups.
Supports AI-Powered Work Summaries
Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
oThese summaries are stored and accessible for later reference.
Links with Service Cloud for Case Resolution
If a conversation escalates into a case, the Messaging Session object can be linked to it.
oThis allows support teams to review the conversation history without switching contexts.
Why Not the Other Options?
Question #164
An Agentforce is creating a custom action in Agent.
Which option is available for the Agentforce Specialist to choose for the custom copilot action?
- A . Apex trigger
- B . SOQL
- C . Flows
Correct Answer: C
C
Explanation:
When creating a custom action in Agent, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot’s functionality without needing custom code.
While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Agent because they are declarative and highly adaptable.
For further guidance, refer to Salesforce Flow documentation and Agent customization resources.
C
Explanation:
When creating a custom action in Agent, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot’s functionality without needing custom code.
While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Agent because they are declarative and highly adaptable.
For further guidance, refer to Salesforce Flow documentation and Agent customization resources.
Question #165
Universal Containers implemented Agent for its users.
One user complains that Agent is not deleting activities from the past 7 days.
What is the reason for this issue?
- A . Agent Delete Record Action permission is not associated to the user.
- B . Agent does not have the permission to delete the user’s records.
- C . Agent does not support the Delete Record action.
Correct Answer: C
C
Explanation:
Agent currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user’s request to delete activities from the past 7 days cannot be fulfilled using Agent.
Unsupported Action: The inability to delete records is due to the current limitations of Agent’s supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records.
User Permissions: Even if the user has the necessary permissions to delete records within Salesforce, Agent itself does not have the capability to execute delete operations.
Reference: Salesforce Agentforce Specialist Documentation – Agent Supported Actions:
o Lists the actions that Agent can perform, noting the absence of delete operations.
Salesforce Help – Limitations of Agent:
o Highlights current limitations, including unsupported actions like deleting records.
C
Explanation:
Agent currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user’s request to delete activities from the past 7 days cannot be fulfilled using Agent.
Unsupported Action: The inability to delete records is due to the current limitations of Agent’s supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records.
User Permissions: Even if the user has the necessary permissions to delete records within Salesforce, Agent itself does not have the capability to execute delete operations.
Reference: Salesforce Agentforce Specialist Documentation – Agent Supported Actions:
o Lists the actions that Agent can perform, noting the absence of delete operations.
Salesforce Help – Limitations of Agent:
o Highlights current limitations, including unsupported actions like deleting records.
Question #166
An Agentforce Specialist wants to troubleshoot their Agent’s performance.
Where should the Agentforce Specialist go to access all user interactions with the Agent, including Agent errors, incorrectly triggered actions, and incomplete plans?
- A . Plan Canvas
- B . Agent Settings
- C . Event Logs
Correct Answer: C
C
Explanation:
Comprehensive and Detailed In-Depth
The Agentforce Specialist needs a comprehensive view of user interactions, errors, and action issues for troubleshooting. Let’s evaluate the options.
Option A: Plan Canvas
Plan Canvas in Agent Builder visualizes an agent’s execution plan for a single interaction, useful for design but not for aggregated troubleshooting data like errors or all interactions, making it incorrect.
Option B: Agent Settings
Agent Settings configure the agent (e.g., topics, channels), not provide interaction logs or error details. This is for setup, not analysis, making it incorrect.
Option C: Event Logs
Event Logs in Agentforce (accessible via Setup or Agent Analytics) record all user interactions, including errors, incorrectly triggered actions, and incomplete plans. They provide detailed telemetry (e.g., timestamps, action outcomes) for troubleshooting performance issues, making this the correct answer.
Why Option C is Correct:
Event Logs offer the full scope of interaction data needed for troubleshooting, as per Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Agent Analytics > Event Logs C Details interaction and error logging.
Trailhead: Monitor and Optimize Agentforce Agents C Recommends Event Logs for troubleshooting.
Salesforce Help: Agentforce Performance C Confirms logs for diagnostics.
C
Explanation:
Comprehensive and Detailed In-Depth
The Agentforce Specialist needs a comprehensive view of user interactions, errors, and action issues for troubleshooting. Let’s evaluate the options.
Option A: Plan Canvas
Plan Canvas in Agent Builder visualizes an agent’s execution plan for a single interaction, useful for design but not for aggregated troubleshooting data like errors or all interactions, making it incorrect.
Option B: Agent Settings
Agent Settings configure the agent (e.g., topics, channels), not provide interaction logs or error details. This is for setup, not analysis, making it incorrect.
Option C: Event Logs
Event Logs in Agentforce (accessible via Setup or Agent Analytics) record all user interactions, including errors, incorrectly triggered actions, and incomplete plans. They provide detailed telemetry (e.g., timestamps, action outcomes) for troubleshooting performance issues, making this the correct answer.
Why Option C is Correct:
Event Logs offer the full scope of interaction data needed for troubleshooting, as per Salesforce documentation.
Reference: Salesforce Agentforce Documentation: Agent Analytics > Event Logs C Details interaction and error logging.
Trailhead: Monitor and Optimize Agentforce Agents C Recommends Event Logs for troubleshooting.
Salesforce Help: Agentforce Performance C Confirms logs for diagnostics.
Question #167
What considerations should an Agentforce Specialist be aware of when using Record Snapshots grounding in a prompt template?
- A . Activities such as tasks and events are excluded.
- B . Empty data, such as fields without values or sections without limits, is filtered out.
- C . Email addresses associated with the object are excluded.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses. However, there are specific limitations to consider. Let’s analyze each option based on official documentation.
Option A: Activities such as tasks and events are excluded.
According to Salesforce Agentforce documentation, when grounding a prompt template with Record
Snapshots, the data included is limited to the record’s fields and certain related objects accessible via Data Cloud or direct Salesforce relationships. Activities (tasks and events) are not included in the snapshot because they are stored in a separate Activity object hierarchy and are not directly part of the primary record’s data structure. This is a key consideration for an Agentforce Specialist, as it means the AI won’t have visibility into task or event details unless explicitly provided through other grounding methods (e.g., custom queries). This limitation is accurate and critical to understand.
Option B: Empty data, such as fields without values or sections without limits, is filtered out.
Record Snapshots include all accessible fields on the record, regardless of whether they contain values. Salesforce documentation does not indicate that empty fields are automatically filtered out when grounding a prompt template. The Atlas Reasoning Engine processes the full snapshot, and empty fields are simply treated as having no data rather than being excluded. The phrase "sections without limits" is unclear but likely a typo or misinterpretation; it doesn’t align with any known Agentforce behavior. This option is incorrect.
Option C: Email addresses associated with the object are excluded.
There’s no specific exclusion of email addresses in Record Snapshots grounding. If an email field (e.g., Contact.Email or a custom email field) is part of the record and accessible to the running user, it is included in the snapshot. Salesforce documentation does not list email addresses as a restricted data type in this context, making this option incorrect.
Why Option A is Correct:
The exclusion of activities (tasks and events) is a documented limitation of Record Snapshots grounding in Agentforce. This ensures specialists design prompts with awareness that activity-related context must be sourced differently (e.g., via Data Cloud or custom logic) if needed. Options B and C do not reflect actual Agentforce behavior per official sources.
Reference: Salesforce Agentforce Documentation: Prompt Templates > Grounding with Record Snapshots C Notes that activities are not included in snapshots.
Trailhead: Ground Your Agentforce Prompts C Clarifies scope of Record Snapshots data inclusion.
Salesforce Help: Agentforce Limitations C Details exclusions like activities in grounding mechanisms.
A
Explanation:
Comprehensive and Detailed In-Depth
Record Snapshots grounding in Agentforce prompt templates allows the AI to access and use data from a specific Salesforce record (e.g., fields and related records) to generate contextually relevant responses. However, there are specific limitations to consider. Let’s analyze each option based on official documentation.
Option A: Activities such as tasks and events are excluded.
According to Salesforce Agentforce documentation, when grounding a prompt template with Record
Snapshots, the data included is limited to the record’s fields and certain related objects accessible via Data Cloud or direct Salesforce relationships. Activities (tasks and events) are not included in the snapshot because they are stored in a separate Activity object hierarchy and are not directly part of the primary record’s data structure. This is a key consideration for an Agentforce Specialist, as it means the AI won’t have visibility into task or event details unless explicitly provided through other grounding methods (e.g., custom queries). This limitation is accurate and critical to understand.
Option B: Empty data, such as fields without values or sections without limits, is filtered out.
Record Snapshots include all accessible fields on the record, regardless of whether they contain values. Salesforce documentation does not indicate that empty fields are automatically filtered out when grounding a prompt template. The Atlas Reasoning Engine processes the full snapshot, and empty fields are simply treated as having no data rather than being excluded. The phrase "sections without limits" is unclear but likely a typo or misinterpretation; it doesn’t align with any known Agentforce behavior. This option is incorrect.
Option C: Email addresses associated with the object are excluded.
There’s no specific exclusion of email addresses in Record Snapshots grounding. If an email field (e.g., Contact.Email or a custom email field) is part of the record and accessible to the running user, it is included in the snapshot. Salesforce documentation does not list email addresses as a restricted data type in this context, making this option incorrect.
Why Option A is Correct:
The exclusion of activities (tasks and events) is a documented limitation of Record Snapshots grounding in Agentforce. This ensures specialists design prompts with awareness that activity-related context must be sourced differently (e.g., via Data Cloud or custom logic) if needed. Options B and C do not reflect actual Agentforce behavior per official sources.
Reference: Salesforce Agentforce Documentation: Prompt Templates > Grounding with Record Snapshots C Notes that activities are not included in snapshots.
Trailhead: Ground Your Agentforce Prompts C Clarifies scope of Record Snapshots data inclusion.
Salesforce Help: Agentforce Limitations C Details exclusions like activities in grounding mechanisms.
Question #168
Universal Containers (UC) has implemented Generative AI within Salesforce to enable summarization of a custom object called Guest. Users have reported mismatches in the generated information.
In refining its prompt design strategy, which key practices should UC prioritize?
- A . Enable prompt test mode, allocate different prompt variations to a subset of users for evaluation, and standardize the most effective model based on performance feedback.
- B . Create concise, clear, and consistent prompt templates with effective grounding, contextual role-playing, clear instructions, and iterative feedback.
- C . Submit a prompt review case to Salesforce and conduct thorough testing In the playground to refine outputs until they meet user expectations.
Correct Answer: B
B
Explanation:
For Universal Containers (UC) to refine its Generative AI prompt design strategy and improve the accuracy of the generated summaries for the custom object Guest, the best practice is to focus on crafting concise, clear, and consistent prompt templates.
This includes:
Effective grounding: Ensuring the prompt pulls data from the correct sources.
Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.
Clear instructions: Giving unambiguous directions on what to include in the response.
Iterative feedback: Regularly testing and adjusting prompts based on user feedback.
Option B is correct because it follows industry best practices for refining prompt design.
Option A (prompt test mode) is useful but less relevant for refining prompt design itself.
Option C (prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.
Reference: Salesforce Prompt Design Best Practices:
https://help.salesforce.com/s/articleView?id=sf.prompt_design_best_practices.htm
B
Explanation:
For Universal Containers (UC) to refine its Generative AI prompt design strategy and improve the accuracy of the generated summaries for the custom object Guest, the best practice is to focus on crafting concise, clear, and consistent prompt templates.
This includes:
Effective grounding: Ensuring the prompt pulls data from the correct sources.
Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.
Clear instructions: Giving unambiguous directions on what to include in the response.
Iterative feedback: Regularly testing and adjusting prompts based on user feedback.
Option B is correct because it follows industry best practices for refining prompt design.
Option A (prompt test mode) is useful but less relevant for refining prompt design itself.
Option C (prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.
Reference: Salesforce Prompt Design Best Practices:
https://help.salesforce.com/s/articleView?id=sf.prompt_design_best_practices.htm
Question #169
An Agentforce is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends.
What functionality allows the AX Specialist to achieve this?
- A . User Utterances dashboard
- B . Agent Event Logs dashboard
- C . AI Audit & Feedback Data dashboard
Correct Answer: A
A
Explanation:
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the Agentforce Specialist to:
Identify Common Queries: Surface frequently asked questions or recurring issues.
Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
Why Other Options Are Incorrect:
B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.
C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.
Reference: Salesforce Einstein Agentforce Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.
Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.
Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.
A
Explanation:
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the Agentforce Specialist to:
Identify Common Queries: Surface frequently asked questions or recurring issues.
Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
Why Other Options Are Incorrect:
B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.
C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.
Reference: Salesforce Einstein Agentforce Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.
Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.
Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.
Question #170
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?
- A . Create the serving endpoint in Databricks, then configure the model using Model Builder.
- B . Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
- C . Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
Correct Answer: A
A
Explanation:
To integrate inferences from an XGBoost model into Salesforce’s Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference. Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio’s Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
Reference: "Einstein Studio and Model Integration with External Endpoints | Salesforce Trailhead" .
A
Explanation:
To integrate inferences from an XGBoost model into Salesforce’s Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference. Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio’s Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
Reference: "Einstein Studio and Model Integration with External Endpoints | Salesforce Trailhead" .