Agentforce-Specialist Salesforce Certified Agentforce Specialist (AI-201) Free Practice Exam Questions (2025 Updated)

Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.

While it might seem intuitive to use the User related list to provide context to the LLM, Salesforce prevents this to ensure that sensitive user information is not inadvertently exposed or misused.

Therefore, the Agentforce Specialist needs to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.

UC’s agent answers questions using Knowledge articles, configured in Agent Builder. Let’s identify the topic and action.

Option A: General Q&A topic and Knowledge Article Answers action."General Q&A" is not a standard topic name in Agentforce, and "Knowledge Article Answers" isn’t a predefined action. This lacks specificity and doesn’t match documentation, making it incorrect.

Option B: General CRM topic and Answers Questions with LLM Action."General CRM" isn’t a default topic, and "Answers Questions with LLM" suggests raw LLM responses, not Knowledge-grounded ones. This doesn’t align with the Knowledge focus, making it incorrect.

Option C: General FAQ topic and Answers Questions with Knowledge Action.In Agent Builder, the "General FAQ" topic is a common default or starting point for question-answering agents. The "Answers Questions with Knowledge" action (sometimes styled as "Answer with Knowledge") is a prebuilt action that retrieves and grounds responses with Knowledge articles. This matches UC’s implementation and is explicitly supported in documentation, making it the correct answer.

Why Option C is Correct:

"General FAQ" and "Answers Questions with Knowledge" are the standard topic-action pair for Knowledge-based question answering in Agentforce, per Salesforce resources.

The AgentForce Observability and Diagnostics Guide details that AgentForce Session Tracing provides the most comprehensive visibility into agent operations. The documentation explains: “Session Tracing captures the entire execution flow for each agent session — including reasoning engine decisions, executed actions, prompts, gateway inputs and outputs, error logs, and final agent responses — to provide an end-to-end view of agent behavior.”

Agentforce Analytics (Option A) focuses on aggregated performance metrics like usage, engagement, and accuracy trends rather than deep operational data.

Utterance Analysis (Option B) evaluates specific interactions or conversation snippets but does not include reasoning engine or system-level traces.

Hence, Option C — AgentForce Session Tracing — is correct as it provides detailed, end-to-end diagnostic insight across all agent executions.

References (AgentForce Documents / Study Guide):

AgentForce Observability Guide: “Using Session Tracing for End-to-End Agent Visibility”

AgentForce Implementation Handbook: “Tracing Reasoning and Action Flows”

AgentForce Study Guide: “Monitoring and Debugging with Session Tracing”

When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.

Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.

Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.

For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.

The root cause of users receiving incorrect sales summaries lies in non-unique action instructions (Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align with the action instructions defined for each action. If the instructions for the three custom actions overlap or lack specificity, the bot’s natural language processing (NLP) cannot reliably distinguish between them, leading to mismatched responses.

Steps to Investigate:

Review Action Instructions: Ensure each custom action has distinct, context-specific instructions. For example:

Action 1: "Summarize quarterly sales by region."

Action 2: "Generate a product-wise sales breakdown for the current fiscal year."

Action 3: "Provide a comparison of sales performance between online and in-store channels."Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.

Test Utterance Matching: Use Einstein Bot’s training tools to validate if user utterances map to the correct action. Overlap indicates instruction ambiguity.

Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to improve intent detection.

Why Other Options Are Incorrect:

A. Assigning actions to an agent is irrelevant, as custom actions are automated bot components.

C. Input/output types relate to data formatting, not intent routing. While important for execution, they don’t resolve utterance mismatches.

Einstein Bot Developer Guide: Stresses the need for unique action instructions to avoid intent conflicts.

Trailhead Module: "Build AI-Powered Bots with Einstein" highlights instruction specificity for accurate action triggering.

Salesforce Help Documentation: Recommends testing and refining action instructions to ensure clarity in utterance mapping.

Context of the Question

Universal Containers (UC) has generative AI–enabled fields that users can access in the desktop experience.

The Agentforce Specialist needs these same fields to be visible and usable in the Salesforce Mobile App.

Why Dynamic Forms on Mobile?

Dynamic Forms allow you to configure record pages so that fields and sections can appear or be hidden based on certain criteria.

When you enable “Dynamic Forms for Mobile,” any generative AI–enabled fields placed on the dynamic layout become accessible in the Salesforce mobile experience.

There is no standard Setup option labeled “Enable Mobile Generative AI” or “Enable Mobile Prompt Responses” as a universal toggle; the existing official approach is to ensure dynamic forms (and the relevant fields) are supported on mobile.

Conclusion

Ensuring that these AI-driven fields are visible on mobile is accomplished by turning on Dynamic Forms on Mobile and adding those fields to the dynamic layout. Therefore, Option C is correct.

Salesforce Agentforce Specialist References & Documents

Salesforce Documentation: Dynamic Forms OverviewExplains how to enable Dynamic Forms for both desktop and mobile UIs, allowing newly added fields (including generative AI–enabled ones) to display in the Salesforce Mobile App.

Salesforce Agentforce Specialist Study GuideReiterates that to expose generative AI fields or components in mobile, you must configure dynamic forms and ensure compatibility on mobile layouts.

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.

When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.

This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.

Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.

Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.

Salesforce Agentforce Specialist References:

You can refer to this documentation for further insights: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm

As per the AgentForce Deployment and Lifecycle Management Guide, AgentForce agents, including their topics, actions, and prompt templates, can be deployed from sandbox to production using Salesforce change sets.

Administrators should create an outbound change set in the sandbox environment that includes all relevant components (Agent definition, prompt templates, topic configurations, flows, and permissions) and then upload it to production for validation and deployment. This process ensures consistency, auditability, and proper dependency tracking between environments.

Option A is incorrect because manual recreation is inefficient and error-prone. Option B is not recommended, as JSON export/import is for development backups and advanced metadata operations, not for standard deployments.

Hence, the correct approach per AgentForce’s best practices is Option C – Create an outbound change set and deploy it to production.

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”

In the Salesforce Agentforce Platform, assigning actions to Agents requires mapping them through Topics in Agent Builder. Topics act as the container that links an Agent’s conversational intent with the specific actions it can perform. By first assigning an action to a Topic in Agent Builder, the Agentforce Specialist ensures that the Agent knows when and how to trigger the action during the business process flow.

Comprehensive and Detailed Explanation From Exact Extract:

According to the documentation of agentic patterns and use cases for the AgentForce platform, there are specific agent types aligned to roles—sales, service, employee support, etc. Since the requirement is for sales staff (i.e., a sales context) and the tasks involve scheduling daily tasks and providing explanations for product pricing and deals, the appropriate agent type is a Sales Agent. A Service Agent is customer‑service oriented; an Employee Agent is internal non‑sales worker support. So option C (Sales Agent) is the best fit.

To address security compliance concerns and provide visibility into the prompt text sent to the LLM, how it is masked, and the masked response, the Agentforce Specialist should recommend enabling the audit trail in the Einstein Trust Layer. This feature captures and logs the prompts sent to the large language model (LLM) along with the masking of sensitive information and the AI's response. This audit trail ensures full transparency and compliance with security requirements.

Option A (Einstein Shield Event logs) is focused on system events rather than specific AI prompt data.

Option B (debug logs) would not provide the necessary insight into AI prompt masking or responses.

For further details, refer to Salesforce's Einstein Trust Layer documentation about auditing and security measures.

According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.

The documentation clearly explains:

“When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases.”

This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user’s query.

Option A is incorrect because past cases cannot be used automatically without indexing them.

Option B is incorrect because a DMO is for structured data (tables, numeric fields) and doesn’t support semantic text retrieval.

Therefore, Option C is correct and aligns fully with Salesforce’s documented best practices.

References (AgentForce Documents / Study Guide):

AgentForce Data Configuration Guide: “Using UDMOs for Case-Based Reasoning”

AgentForce Implementation Handbook: “Indexing Historical Case Records for Semantic Search”

AgentForce Study Guide: “Creating Unstructured Data Model Objects from Case Objects”

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)

According to the Salesforce Prompt Builder Best Practices Guide, an effective prompt must include Role, Task, Context, Constraints, and Format clearly defined — a structure known as the RTCCF model. The documentation explains: “Prompts should specify the assistant’s role, define a clear task, include context and constraints, and provide output format instructions to ensure predictable and high-quality responses.”

Option A follows this framework precisely. It defines:

Role: The assistant’s identity (“experienced support agent”).

Task: Summarizing case notes.

Context: Customer issue, troubleshooting steps, next actions.

Constraints: Limit of 5 sentences, plain language, include “No next action required” if applicable.

Format: Numbered sentences for clarity.

Options B and C omit critical prompt design elements such as strict constraints or output formatting and therefore do not align with Salesforce’s prompt design standard.

References (AgentForce Documents / Study Guide):

Salesforce Prompt Builder Guide: “Prompt Structure Using RTCCF Model”

AgentForce Prompt Template Design Guide: “Best Practices for Summarization Prompts”

Salesforce AI Prompt Engineering Study Guide

The Agent-to-Agent (A2A) Protocol Overview describes A2A as a standardized framework for cross-vendor agent discovery and communication. The documentation specifies:

“A2A enables secure, interoperable communication between AI agents across vendors, platforms, and ecosystems, using standardized APIs and schemas for message exchange and capability discovery.”

This allows AgentForce agents to interact with external AI systems or partner agents while maintaining data governance and identity controls.

Option B is incorrect because auto-onboarding without contracts or trust verification is not supported.

Option C confuses A2A with the internal reasoning runtime used by AgentForce; A2A operates across systems, not within a single platform.

Therefore, Option A correctly defines the key benefit of the Agent-to-Agent protocol.

References (AgentForce Documents / Study Guide):

AgentForce Architecture Guide: “Understanding the Agent-to-Agent (A2A) Protocol”

AgentForce Interoperability Handbook: “Cross-Vendor Agent Communication Framework”

AgentForce Study Guide: “A2A Integration Standards and Benefits”

The hotel resort team needs an AI-generated guest summary with recommendations, displayed exclusively on the contact record page. Let’s assess the options.

Option A: Model BuilderModel Builder in Salesforce creates custom predictive AI models (e.g., for scoring or classification) using Data Cloud or Einstein Platform data. It’s not designed for generating text summaries or embedding them on record pages, making it incorrect.

Option B: Agent BuilderAgent Builder in Agentforce Studio creates autonomous AI agents for tasks like lead qualification or customer service. While agents can provide summaries, they operate in conversational interfaces (e.g., chat), not as static content on a record page. This doesn’t meet the location-specific requirement, making it incorrect.

Option C: Prompt BuilderEinstein Prompt Builder allows creation of prompt templates that generate text (e.g., summaries, recommendations) using Generative AI. The template can pull data from contact records (e.g., activity preferences) and be embedded as a Lightning component on the contact record page via a Flow or Lightning App Builder. This ensures the summary is available only where specified, meeting the team’s needs perfectly and making it the correct answer.

Why Option C is Correct:

Prompt Builder’s ability to generate contextual summaries and integrate them into specific record pages via Lightning components aligns with the team’s requirements, as supported by Salesforce documentation.

Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment. Let’s evaluate the options based on official Salesforce documentation.

Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is designed to engage prospects in real-time conversations, primarily through the Messaging channel (e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making this a key consideration UC must be aware of. This is the correct answer.

Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email. While it can send emails (e.g., follow-ups after lead qualification), its primary function—real-time lead engagement—relies on Messaging. Stating it "only works in the Email channel" is inaccurate and contradicts its documented capabilities, making this incorrect.

Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation choice, not a mandatory requirement. The agent’s deployment is channel-specific (Messaging), and website integration is optional, not a "must." This option overstates the requirement, making it incorrect.

Why Option A is Correct:

The SDR Agent’s primary deployment in the Messaging channel is a documented consideration for its real-time lead qualification capabilities. UC must plan for this channel to ensure effective implementation, as per Salesforce guidelines.