Free Practice Questions for Salesforce Agentforce-Specialist Exam

In Salesforce Agentforce, custom Agent actions are designed to enable AI-driven agents to perform specific tasks within a conversational context. Action Instructions are a critical component when creating these actions because they define the expected user experience by outlining how the action should behave, what it should accomplish, and how it interacts with the end user. These instructions act as a blueprint for the action’s functionality, ensuring that it aligns with the intended outcome and provides a consistent, intuitive experience for users interacting with the agent. For example, if the action is to "schedule a meeting," the Action Instructions might specify the steps (e.g., gather date and time, confirm with the user) and the tone (e.g., professional, concise), shaping the user experience.

Option B: While Action Instructions might indirectly influence how a user invokes an action (e.g., by making it clear what inputs are needed), they are not primarily about telling the user how to call the action in a conversation. That’s more related to user training or interface design, not the instructions themselves.

Option C: The large language model (LLM) relies on prompts, parameters, and grounding data to determine which action to execute, not the Action Instructions directly. The instructions guide the action’s design, not the LLM’s decision-making process at runtime.

Thus, Option A is correct as it emphasizes the role of Action Instructions in defining the user experience, which is foundational to creating effective custom Agent actions in Agentforce.

Salesforce Agentforce Documentation: "Create Custom Agent Actions" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_custom_actions.htm &type=5)

Trailhead: "Agentforce Basics" module (https://trailhead.salesforce.com/content/learn/modules/agentforce-basics)

Context of the QuestionUniversal Containers (UC) wants to include details from a related list on the Account object in a prompt template. This is typically done via Prompt Builder in Salesforce’s generative AI setup.

Prompt Builder Behavior

Selecting a Related List: Within Prompt Builder, you can navigate to the object (Account) and choose which related list (e.g., Contacts, Opportunities) you want to reference.

Field Picker: Once a related list is chosen, Prompt Builder provides a field picker interface, allowing you to select specific fields from that related list. These fields then become available for merge fields or dynamic insertion within your prompt.

Why Option A is Correct

Direct Alignment with the Standard Process: The recommended approach in Salesforce’s documentation is to select a related list and then use the field picker to add the necessary fields into your AI prompt. This ensures the prompt has exactly the data you need from that related list.

Why Not Option B (JSON Formatting)

No Mandatory JSON Requirement: Although you can structure data as JSON if you desire advanced formatting, Prompt Builder does not require you to manually assign the fields from the related list in JSON. The platform automatically handles how the data is passed along in the background.

Why Not Option C (Default Page Layout)

Independent of Page Layout: Prompt Builder does not rely strictly on the default page layout for fields. You can configure the fields you want from the related list, independent of how the user’s page layout is set up in the UI.

ConclusionSince the official Salesforce approach involves selecting a related list and then using the field picker to insert merge fields, Option A is the correct and verified answer.

Salesforce Agentforce Specialist References & Documents

Salesforce Official Documentation: Prompt Builder BasicsExplains how to reference objects and related lists when building AI prompts.

Salesforce Trailhead: Get Started with Prompt BuilderProvides hands-on exercises demonstrating how to pick fields from related objects or lists.

Salesforce Agentforce Specialist Study GuideOutlines best practices for referencing related records and fields in generative AI prompts.

According to the AgentForce Testing Center Guide, structured batch testing is the feature that provides consistent, repeatable pass/fail validation for agent testing. The documentation explains: “Structured batch testing allows specialists to define expected outputs per test utterance and automatically validate the agent’s responses, resulting in deterministic pass/fail outcomes.”

This approach ensures testing consistency across multiple runs and environments, unlike user ratings (Option A) which are subjective, or event log scripts (Option B) which are manual and not standardized.

Thus, Option C is correct because structured batch testing provides Salesforce’s official framework for validating agent accuracy with consistent logic.

References (AgentForce Documents / Study Guide):

AgentForce Testing Center Guide: “Running Structured Batch Tests”

AgentForce QA and Validation Best Practices

AgentForce Study Guide: “Automated Pass/Fail Validation for AI Agents”

The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.

For instance, an AgentForce retriever could be configured to include only documents tagged as “Active” or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.

Option B is incorrect because filters do not handle encryption or masking of sensitive data — those functions are managed through Data Cloud security and access controls. Option C is incorrect because retrievers do not aggregate or summarize documents; they retrieve data for grounding, leaving summarization to the LLM reasoning layer.

Therefore, the correct answer is Option A – Filters narrow search results using field-based conditions to improve relevancy and retrieval precision.

The AgentForce Security and Access Control Best Practices Guide emphasizes the principle of least privilege, which means granting each agent only the permissions strictly necessary to perform its defined tasks.

To prevent unauthorized access to sensitive data such as medical research, administrators should exclude permissions for the Medical Product object and related records from the AgentForce Service Agent’s permission set group. This approach ensures that even if the reasoning engine processes a related query, it cannot retrieve or expose data it lacks access to.

Option A is partially effective but not sufficient since Salesforce role hierarchy does not fully restrict record access. Option B is over-restrictive and would prevent legitimate operations involving other custom objects.

Thus, the correct answer is Option C – Follow the principle of least privilege and avoid granting permission to view the Medical Product object or related records.

Agentforce Sales Agent provides real-time insights and AI-powered recommendations, which are designed to streamline the sales process and help sales representatives focus on key tasks to increase conversion rates. It offers features like lead scoring, opportunity prioritization, and proactive recommendations, ensuring that sales teams can interact with customers efficiently and close deals faster.

Option A: While tracking customer interactions is beneficial, it is only part of the broader capabilities offered by Agentforce Sales Agent and is not the primary objective for improving real-time productivity.

Option B: Agentforce Sales Agent does not automate the entire sales process but provides actionable recommendations to assist the sales team.

Option C: This aligns with the tool's core purpose of enhancing productivity and driving sales success.

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the AgentForce Permissions and Role Management Guide, permission sets control the level of access users have to Prompt Templates — which define how the reasoning engine interprets and generates responses. AgentForce distinguishes between users who create or manage templates and those who only execute or use them during interactions.

The Prompt Execute User permission set is designed for users who can invoke and run prompts but cannot create, modify, or delete prompt templates. This role is intended for front-line users such as sales reps, service agents, or support staff who utilize preconfigured templates in daily workflows without altering them.

The Prompt Template Manager permission set (Option B) grants full administrative access — allowing users to create, edit, and delete templates. This is reserved for system administrators or AgentForce specialists responsible for managing prompt configurations.

The Prompt Template User permission set (Option C) provides limited management capabilities, enabling viewing and cloning of templates but still allowing minor modifications, which does not meet the stated restriction.

Therefore, to ensure sales reps can only use but not edit or create prompt templates, the correct permission set is Option A – Prompt Execute User.

How Does Data Cloud Support "Answer Questions with Knowledge" in Agentforce?

The Answer Questions with Knowledge action in Agentforce leverages Salesforce Data Cloud to store, manage, and index Knowledge articles used for AI-powered responses.

Data Cloud as the Central Storage for Knowledge Articles

Indexed Knowledge articles are stored and retrieved in real-time from Data Cloud.

The AI system queries Data Cloud to fetch relevant articles when a service agent or customer needs an answer.

Ensuring Up-to-Date Responses

Data Cloud continuously updates Knowledge articles based on new insights, user interactions, and feedback.

The AI can pull the latest, most relevant information from the Knowledge base.

Enhancing AI-Driven Customer Service

AI-generated responses are grounded in real customer service interactions.

Service agents benefit from fast, context-aware answers, improving resolution times and customer satisfaction.

Why Not the Other Options?

❌ A. Data Cloud is not required; the articles can be accessed directly from the CRM by the agent.

Incorrect because Data Cloud is the primary system for storing and indexing Knowledge articles.

Without Data Cloud, Einstein AI cannot efficiently retrieve and rank articles dynamically.

❌ C. Data Cloud provides the real-time data streams that update the Knowledge articles.

Incorrect because while Data Cloud stores and manages articles, real-time updates are not its primary function.

The Knowledge Management system within Salesforce handles article creation and updates.

Agentforce Specialist References

Salesforce AI Specialist Material highlights that Data Cloud is the core storage system for AI-driven Knowledge management​.

Salesforce Instructions for Certification confirm the central role of Data Cloud in managing indexed Knowledge articles for AI-powered responses​.

In the AgentForce Flex Prompt Design Guide, the recommended best practice for Flex prompt templates is to ensure that the large language model (LLM) is provided with clear contextual information and a defined role (e.g., sales rep, support agent, concierge). Flex prompts are intentionally open-ended and adaptable, enabling them to handle dynamic and conversational use cases, provided they are guided with sufficient context and role clarity.

By setting a role and including detailed contextual cues, the LLM can tailor tone, content, and reasoning appropriately while maintaining response accuracy.

Option B describes a data-grounded Record Summary use case, not a Flex prompt. Option C, involving retrievers, is for grounding responses to external or indexed data — but Flex prompts typically handle free-form or reasoning-driven tasks, not retrieval-based ones.

Thus, following AgentForce’s official design standards, the correct best practice is Option A – Provide contextual information and assign a role to the LLM for optimal performance and consistency.

Why is "Sales rep users are missing the Use SDR Agent permission set" the correct answer?

If sales reps are unable to find the Agentforce Sales Development Representative (SDR) Agent, the most likely cause is missing permissions. The "Use SDR Agent" permission set is required for users to access and interact with the SDR Agent in Agentforce.

Key Considerations for This Issue:

Permission Set Restriction

Users must have the "Use SDR Agent" permission set to access Agentforce SDR in their Salesforce environment.

If they lack this permission, the SDR Agent will not appear in their interface.

Agentforce Role-Based Access Control

Agentforce assigns specific permissions based on user roles.

Sales reps require explicit permission to access the SDR Agent.

Fixing the Issue

The Salesforce Admin should assign the "Use SDR Agent" permission set to all relevant sales reps.

This is done in Setup → Permission Sets → Assign to Users.

Why Not the Other Options?

❌ A. Sales rep users' profiles are missing the Allow SDR Agent permission.

Incorrect because "Allow SDR Agent" is not a standard permission setting in Agentforce.

Permission is granted via permission sets, not profile-level settings.

❌ B. Sales rep users do not have access to the SDR Agent object.

Incorrect because there is no separate "SDR Agent object" in Salesforce.

SDR Agents are AI-driven features, not standard CRM objects that require object-level access.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that users require specific permission sets to access Agentforce SDR Agents​.

Salesforce Instructions for Certification highlight the role of permission sets in controlling Agentforce access​.