Free Practice Questions for Salesforce Agentforce-Specialist Exam

Per the AgentForce Security and Permission Management Guide, the AgentForceServiceAgentUserPsg (Permission Set Group) controls access privileges for Service Agents, including which Salesforce objects, fields, and related data they can read or interact with.

When extending an agent’s access to additional custom fields or related objects, the documented best practice is to update the existing permission set group assigned to that agent type rather than creating new or profile-based permissions. This approach maintains centralized permission governance, ensures license alignment, and avoids conflicts or redundancy across multiple permission layers.

Option B is incorrect because creating a new permission set with an Einstein Agent License is unnecessary — the permission set group already includes license mappings. Option C is not recommended, as editing the Einstein Agent User Profile can cause system-wide effects and deviate from Salesforce’s principle of least privilege.

Therefore, the correct approach is Option A – Update the Object and Field access in the AgentForceServiceAgentUserPsg permission set group, ensuring secure and proper access for the Service Agent to custom case data.

To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the Agentforce Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.

Option C is correct because it enables visibility into toxic language detection within the Trust Layer and allows for auditing responses for toxicity.

Option A suggests checking a toxicity detection log, but Salesforce provides more comprehensive options via the audit report.

Option B involves creating a flow, which is unnecessary for toxicity detection monitoring.

Salesforce Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm

UC’s issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud’s vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions—such as a filter on a "Last Modified Date" or similar timestamp field—to limit retrieval to documents updated within a recent period (e.g., last 6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.

Option A: Switching to a Knowledge-based Data Library (using Salesforce Knowledge articles) could work, as Knowledge articles have versioning and expiration features to manage recency. However, this assumes UC’s training content is already in Knowledge articles (not PDFs) and requires migrating all uploaded files, which is a significant shift not justified by the question’s context. File-based libraries are still viable with proper filtering.

Option B: This is the best corrective action. A custom retriever with a date filter leverages the existing file-based library, refining retrieval without changing the data source, making it practical and targeted.

Option C: Relying on periodic re-uploads with the default retriever is passive and inefficient. It doesn’t guarantee recency (old files remain indexed until manually removed) and requires ongoing manual effort, failing to proactively solve the issue.

Option B provides a precise, scalable solution to ensure content relevancy in UC’s AI-driven training system.

Salesforce Agentforce Documentation: "Custom Retrievers for Data Libraries" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm &type=5)

Salesforce Data Cloud Documentation: "Filter Retrieval for AI" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_retrieval_filters.htm &type=5)

Trailhead: "Manage Data Libraries in Agentforce" (https://trailhead.salesforce.com/content/learn/modules/agentforce-data-libraries)

According to the AgentForce for Service Configuration Guide, for an agent to interact with and retrieve Knowledge articles, it must have Knowledge object and field access, along with the “Allow View Knowledge” permission. The documentation explains: “Agents need permission to access Knowledge records and their fields to retrieve and summarize content accurately. Additionally, the Allow View Knowledge setting enables the agent to use the Knowledge object as a retrieval source.”

Option A is incomplete because “Run Flows” is unrelated to Knowledge article access. Option C refers to custom object permissions and external user management, which are unrelated to Knowledge configuration. Therefore, Option B provides the correct and required set of permissions for AgentForce Service Agents to access and utilize Knowledge data effectively.

References (AgentForce Documents / Study Guide):

AgentForce for Service Setup Guide: “Knowledge Article Access and Permissions”

Salesforce Knowledge Configuration Guide: “Granting View Knowledge Access to Agents”

AgentForce Study Guide: “Configuring Service Agents for Knowledge Retrieval”

The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: “Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles.”

Option A is incorrect because direct access to data model objects (DMOs) in Flex templates is not supported in Prompt Builder. Option C (external APIs) is unnecessary, as Prompt Builder has native integration with Data Cloud.

Thus, Option B is the correct and Salesforce-documented method to access Data Cloud data from Prompt Builder.

References (AgentForce Documents / Study Guide):

Salesforce Prompt Builder Guide: “Integrating with Data Cloud”

AgentForce Study Guide: “Fetching Data Cloud Data with Prompt-Initiated Flows”

Salesforce Data Cloud Documentation: “Using Related Lists in Prompt Templates”

The AgentForce API Integration Guide defines the Agent API as the framework that enables external web or mobile applications to communicate directly with Salesforce-hosted agents. This API supports message exchange, session management, and context persistence — allowing developers to build custom chat interfaces while maintaining secure, real-time connectivity with the AgentForce reasoning engine.

Option A (A2A) is for inter-agent collaboration within Salesforce, not for external web integration. Option B (MCP) — Model Context Protocol — is used for context sharing between models and tools, not for front-end integration.

Therefore, the correct framework for enabling communication between a custom website chat interface and an AgentForce agent is Option C – Agent API, as it provides the structured interface for external client applications.

Context of the QuestionUniversal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.

Field Generation Prompt Template

Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.

Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.

Why Not Flex or Sales Email Prompt Templates?

Flex Prompt Template: Used to combine or manipulate data across objects, merges, or references from multiple sources in more advanced, flexible prompts. Typically not the go-to for straightforward text generation on a single field.

Sales Email Prompt Template: Focused on drafting or summarizing emails for sales reps (like crafting outreach or follow-up messages). This template is not specifically built to populate a field on a record.

ConclusionFor automatically populating the Description field with AI-generated content, the Field Generation prompt template (Option A) is the correct choice.

Salesforce Agentforce Specialist References & Documents

Salesforce Documentation: Prompt Template TypesExplains various template types (Field Generation, Flex, Email, etc.) and their typical use cases.

Salesforce Agentforce Specialist Study GuideHighlights Field Generation prompt templates for populating or updating record fields with AI-generated text.

According to the AgentForce and Einstein Studio Integration Guide, when a business requires custom ranking or filtering logic (such as by product line and recency), the correct solution is to build a custom retriever in Einstein Studio. The documentation describes: “Custom retrievers in Einstein Studio enable configuration of metadata filters (e.g., product line) and custom ranking functions such as recency or relevance scoring. This allows fine-tuned control over retrieval beyond the default retriever’s capabilities.”

Option A, the default retriever, provides general ranking and does not natively apply custom filters. Option C, applying semantic embeddings with default filters, is useful for general search optimization but lacks custom ranking logic.

Therefore, Option B aligns with Salesforce’s prescribed method for fine-tuned retrieval control in enterprise use cases requiring metadata-based and recency ranking.

References (AgentForce Documents / Study Guide):

AgentForce Einstein Studio Guide: “Building Custom Retrievers with Metadata and Ranking”

AgentForce Data Cloud Configuration Notes: “Filtering and Ranking in Custom Retrieval”

AgentForce Study Guide: “Advanced Retrieval Customization in Einstein Studio”

Per the AgentForce Integration and AI Architecture Guide, the Model Context Protocol (MCP) enables agents to interact dynamically with external predictive or AI models. The documentation states:

“Through MCP, agents can discover, connect, and invoke external models via standardized schema definitions. This allows agents to use third-party tools—like recommendation engines or classifiers—without pre-coding fixed API calls.”

Option A (MCP) supports dynamic interoperability with predictive models, making it the correct answer.

Option B (Hugging Face) refers to a model hosting platform, not a Salesforce integration mechanism.

Option C (A2A protocol) supports agent-to-agent communication, not external model invocation.

Therefore, Option A correctly reflects the Salesforce-recommended method for integrating external predictive APIs.

References (AgentForce Documents / Study Guide):

AgentForce Architecture Guide: “Integrating Predictive Models with MCP”

AgentForce Technical Overview: “Model Context Protocol for External Tools”

AgentForce Study Guide: “Using MCP for Recommendation and AI Model Access”