QUESTION: 37

What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?

Find similar requested topics and provide the actions that need to be executed.
Identify the best matching topic and actions and correct order of execution.
Determine a user's topic access and sort actions by priority to be executed.

Answer(s): B

Explanation:

In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions. Let's evaluate its role.

Option A: Find similar requested topics and provide the actions that need to be executed.

While the LLM can identify similar topics, its role extends beyond merely finding them--it matches intents to specific topics and determines execution. This option understates the LLM's responsibility for ordering actions, making it incomplete and incorrect.

Option B: Identify the best matching topic and actions and correct order of execution.

The LLM analyzes user input to understand intent, matches it to the best-fitting topic (configured in Agent Builder), and selects associated actions. It also determines the correct sequence of execution based on the agent's plan (e.g., retrieve data before updating a record). This end-to-end process-- from intent recognition to action orchestration--is the LLM's core role in Agentforce, making this the correct answer.

Option C: Determine a user's topic access and sort actions by priority to be executed.

Topic access is governed by Salesforce permissions (e.g., user profiles), not the LLM.
While the LLM prioritizes actions within its plan, its primary role is intent matching and execution ordering, not access control, making this incorrect.

Why Option B is Correct:

The LLM's role in identifying topics, selecting actions, and ordering execution is central to Agentforce's autonomous functionality, as detailed in Salesforce documentation.

Reference:

Salesforce Agentforce Documentation: Atlas Reasoning Engine Outlines LLM's intent and action handling.

Trailhead: Understand Agentforce Technology Explains topic matching and execution.

Salesforce Help: Agentforce Actions Confirms LLM's role in orchestrating responses.

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QUESTION: 38

Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles.
When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly.
What should UC do to troubleshoot the issue?

Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.
Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.
Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.

Answer(s): C

Explanation:

UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let's troubleshoot the issue.

Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the

Agentforce Service Agent User.

The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.

Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.

Prompt templates in Prompt Builder don't require specific permissions beyond general Einstein Generative AI access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent's runtime access, not the template itself, making this incorrect.

Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.

When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.

Why Option C is Correct:

The Agentforce Service Agent User's lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.

Reference:

Salesforce Agentforce Documentation: Data Library Setup > Permissions Requires Data Cloud access for agents.

Trailhead: Ground Your Agentforce Prompts Notes Data Cloud User permission for Knowledge grounding.

Salesforce Help: Agentforce Security > Agent User Setup Lists required permission sets.

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QUESTION: 39

Universal Containers' service team wants to customize the standard case summary response from Agentforce.
What should the Agentforce Specialist do to achieve this?

Create a custom Record Summary prompt template for the Case object.
Summarize the Case with a standard Agent action.
Customize the standard Record Summary template for the Case object.

Answer(s): A

Explanation:

UC's service team seeks to customize the standard case summary response provided by Agentforce.
Let's assess the options for tailoring this output.

Option A: Create a custom Record Summary prompt template for the Case object.

In Prompt Builder, the standard Record Summary prompt template generates summaries for objects like Case. To customize it, the Agentforce Specialist can create a new custom prompt template, specifying the Case object as the source, and adjust the instructions (e.g., tone, fields included) to meet UC's needs. This new template can then be invoked by an agent or flow, providing a tailored summary. This approach offers full control and aligns with Salesforce's customization process, making it the correct answer.

Option B: Summarize the Case with a standard Agent action.

Standard Agent actions (e.g., "Answer Questions") don't specifically target case summarization-- they're broader in scope. There's no out-of-the-box "Summarize Case" action that allows customization of the response format, making this insufficient and incorrect.

Option C: Customize the standard Record Summary template for the Case object.

Standard prompt templates in Prompt Builder (e.g., Record Summary) are read-only and cannot be directly edited. Customization requires cloning or creating a new template, not modifying the standard one, making this incorrect.

Why Option A is Correct:

Creating a custom Record Summary prompt template allows full customization of the case summary, leveraging Prompt Builder's flexibility, as per Salesforce best practices.

Reference:

Salesforce Agentforce Documentation: Prompt Builder > Custom Templates Details creating custom summaries.

Trailhead: Build Prompt Templates in Agentforce Explains customizing standard outputs.

Salesforce Help: Record Summaries with AI Recommends custom templates for tailored results.

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QUESTION: 40

Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the

"Answer Questions with Knowledge" action. How should UC achieve this?

Define scope instructions to the agent specifying a list of allowed article titles or IDs.
Update the Data Library Retriever to filter on a custom field on the Knowledge article.
Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.

Answer(s): C

Explanation:

UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles. Let's evaluate the options for scoping agent access.

Option A: Define scope instructions to the agent specifying a list of allowed article titles or IDs.

Agent instructions in Agent Builder guide behavior but cannot enforce granular data access restrictions like a specific list of article titles or IDs. This approach is impractical and bypasses Salesforce's security model, making it incorrect.

Option B: Update the Data Library Retriever to filter on a custom field on the Knowledge article.

While Data Library Retrievers in Data Cloud can filter data, this requires custom development (e.g., modifying indexing logic) and assumes articles are ingested with a custom field for filtering. This is less straightforward than native Knowledge features and not a standard option, making it incorrect.

Option C: Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.

Salesforce Knowledge uses Data Categories to organize articles (e.g., by topic or type). In Agentforce, when configuring a Data Library with Knowledge, you can apply Data Category filters to limit which articles the agent accesses. For the "Answer Questions with Knowledge" action, this ensures the agent only retrieves articles within the specified categories, aligning with UC's goal. This is a native, documented solution, making it the correct answer.

Why Option C is Correct:

Using Data Categories and filters in the Data Library is the recommended, scalable way to limit Knowledge article access for agent actions, as per Salesforce documentation.

Reference:

Salesforce Agentforce Documentation: Data Library > Knowledge Filters Describes Data Category filtering.

Trailhead: Ground Your Agentforce Prompts Covers limiting Knowledge scope.

Salesforce Help: Knowledge in Agentforce Recommends categories for access control.

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