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

To enable Sales Email prompt templates:

Permission Set: Assign the Prompt Template User permission set to the sales team to grant access to use pre-built templates.

Feature Activation: Enable Sales Emails in Salesforce Setup to activate the integration between prompt templates and email workflows.

Option B (Manager permission set): Required for creating/modifying templates, not for usage.

Option C (Data Cloud Admin): Unrelated to prompt template access.

In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud’s vector database or Salesforce records) to ground AI responses. UC’s issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever’s search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the ‘Policy’ category” or “records created after a certain date”) that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC’s problem effectively.

Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.

Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC’s goal of improving relevance.

Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.

Option A is the most effective step to ensure relevance in retrieved data.

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

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

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.

According to the AgentForce Security and Data Access Configuration Guide, the best practice for ensuring an agent (such as an SDR Agent) can access specific records while maintaining security is to use criteria-based sharing rules. The documentation states: “When an AI Agent needs access to a subset of records (for example, regional leads), create a sharing rule granting access to that agent’s user context based on defined criteria such as region or ownership.”

Option A (assigning the highest-level role) provides excessive access beyond the intended scope, violating the principle of least privilege. Option B (View All permission) gives global object access, which is not secure. Therefore, Option C ensures that the SDR Agent has controlled, region-specific access to lead records.

References (AgentForce Documents / Study Guide):

AgentForce Security and Access Guide: “Using Sharing Rules for AI Agent Access Control”

AgentForce Implementation Handbook: “Regional Access Configuration for SDR Agents”

Salesforce Data Security Study Guide

UC wants insights into product and competitor mentions during sales calls, leveraging Einstein Conversation Insights. Let’s evaluate the options.

Option A: Enable Einstein Conversation Insights, connect a recording provider, assign permission sets, and customize insights with up to 25 products.Einstein Conversation Insights analyzes call recordings to identify keywords like product and competitor names. Setup requires enabling the feature, connecting an external recording provider (e.g., Zoom, Gong), assigning permission sets (e.g., Einstein Conversation Insights User), and customizing insights by defining up to 25 products or competitors to track. Salesforce documentation confirms the 25-item limit for custom keywords, making this the correct, precise answer aligning with UC’s needs.

Option B: Enable Einstein Conversation Insights, assign permission sets, define recording managers, and customize insights with up to 50 competitor names.There’s no "recording managers" role in Einstein Conversation Insights setup—integration is with a provider, not a manager designation. The limit is 25 keywords (not 50), and the option omits the critical step of connecting a provider, making it incorrect.

Option C: Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products."Enable sales recording" is vague—Conversation Insights relies on external providers, not a native Salesforce recording feature. The keyword limit is 25, not 50, making this incorrect despite being closer than B.

Why Option A is Correct:

Option A accurately reflects the setup process and limits for Einstein Conversation Insights, meeting UC’s requirement per Salesforce documentation.

The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce’s AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:

Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.

Why not B? Using Prompt Builder in Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.

Why not C? Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs.

More information on auditing AI interactions can be found in the Salesforce AI Trust Layer documentation, which outlines how organizations can manage and track generative AI interactions securely.

Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.

Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC’s first goal.

Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC’s second goal.

Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don’t directly reduce post-chat field entry time.

Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.

Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).

Thus, Option A is the correct answer for UC’s needs.

Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm &type=5)

Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.case_classification.htm &type=5)

Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce-for-service)

Context of the QuestionUniversal Containers (UC) uses Agentforce, a specialized AI-driven assistant for Salesforce. A user reports that an Agent is unable to delete recent activities.

Why Agentforce Cannot Delete Records

Agentforce’s Standard Actions: Agentforce typically has predefined or “standard” actions like Create, Update, or Summarize records. However, a standard Delete Record action is not part of the default set of Agentforce actions.

Implication: If Agentforce has no built-in delete functionality, it cannot remove activities—even if the user has permission to delete them in the Salesforce UI.

Why Other Options Are Incorrect

Option A – Permission to Delete the User’s Records: Standard Salesforce user permissions do not automatically extend to Agentforce’s capabilities. Even if the user can delete records, that doesn’t grant Agentforce a new action.

Option B – Agentforce Delete Record Action Permission: There is no separate “Delete Record Action permission” for Agentforce to be toggled. The relevant issue is that the standard Delete Record action does not exist within Agentforce out of the box.

ConclusionThe core reason for the issue is that Agentforce does not support a standard Delete Record action (Choice C).

Salesforce Agentforce Specialist References & Documents

Salesforce Official Documentation – Agentforce(Note: Agentforce may be a pilot or specialized feature; check pilot release notes or official docs for standard actions.)

Salesforce Agentforce Specialist Study GuideCovers the limitations of certain AI-enabled features regarding record operations.

Why is "Identifying agent topics and actions to respond to user utterances" the correct answer?

In Agentforce, the reasoning engine plays a critical role in interpreting user queries and determining the appropriate agent response.

Key Functions of the Reasoning Engine in Agentforce:

Analyzing User Intent

The reasoning engine interprets the meaning behind natural language user inputs.

It maps user utterances to predefined topics to determine the correct AI-generated response.

Selecting the Appropriate Agent Action

The engine evaluates available actions and selects the best response based on the detected topic.

For example, if a user asks, "What is my current account balance?", the reasoning engine:

Identifies the topic: "Account Information"

Chooses the correct action: "Retrieve account balance"

Executes the action and returns the response

Ensuring AI Accuracy and Context Awareness

The reasoning engine grounds AI-generated responses in relevant Salesforce data, ensuring accurate outputs.

Why Not the Other Options?

❌ B. Offering real-time natural language response during conversations.

Incorrect because real-time natural language processing (NLP) is handled by the large language model (LLM), not the reasoning engine.

The reasoning engine focuses on action selection, not linguistic processing.

❌ C. Generating record queries based on conversation history.

Incorrect because query generation is handled by Copilot Actions (e.g., Query Records), not the reasoning engine.

The reasoning engine decides which query should be run, but does not generate queries itself.

Agentforce Specialist References

Salesforce AI Specialist Material explains that the reasoning engine identifies topics and selects agent actions​.

Salesforce Instructions for the Certification confirm that the reasoning engine determines AI workflow execution​.

Before enabling Call Explorer, the Salesforce Agentforce Specialist must ensure that the Einstein Conversation Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and actionable insights.

Key Considerations:

Permission Set Requirement:

Users (including admins) need the Einstein Conversation Insights permission set to access and use Call Explorer. Without this, the feature remains inaccessible.

The permission set grants access to ECI tools, including call transcription, analysis, and dashboard visibility.

Why Other Options Are Incorrect:

A. Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission set, not Knowledge configuration.

B. Custom Actions: Call Explorer does not require custom actions to be built before configuration. It is a pre-built analytics tool that works once permissions and data sources (e.g., call recordings) are configured.

Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation Insights permission set is required to access Call Explorer.

Trailhead Module: "Einstein Conversation Insights Basics" outlines permission prerequisites for enabling call analytics.

Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI permissions.

Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI-generated plans that provide guidance and steps for closing deals. Let’s evaluate the options based on Agentforce for Sales features.

Option A: Create Account PlanWhile account planning is valuable for long-term strategy, Agentforce for Sales does not have a specific "Create Account Plan" feature focused on closing individual deals. Account plans typically involve broader account-level insights, not deal-specific closure steps, making this incorrect for UC’s requirement.

Option B: Find Similar Deals"Find Similar Deals" is not a documented feature in Agentforce for Sales. It might imply identifying past deals for reference, but it doesn’t involve generating plans with guidance and steps for closing current deals. This option is incorrect and not aligned with UC’s goal.

Option C: Create Close PlanThe "Create Close Plan" feature in Agentforce for Sales uses AI to generate a detailed plan with actionable steps and guidance tailored to closing a specific deal. Powered by the Atlas Reasoning Engine, it analyzes deal data (e.g., Opportunity records) and provides reps with a roadmap to meet quotas. This directly meets UC’s requirement for AI-generated plans focused on deal closure, making it the correct answer.

Why Option C is Correct:

"Create Close Plan" is a specific Agentforce for Sales capability designed to help reps close deals with AI-driven plans, aligning perfectly with UC’s needs as per Salesforce documentation.

When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.

Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.

Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.

The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.

Salesforce Agentforce Specialist References:

For more information, refer to: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm

The AgentForce Testing Center Implementation Guide specifies that organizations should perform all structured and regression testing in a sandbox environment replicated from production. The guide explains:

“Testing Center supports running tests safely in sandbox environments that mirror production data. This ensures realistic test conditions without impacting live systems or data.”

Option A (developer orgs) provides limited and unrepresentative data.

Option B (production org) is not recommended due to potential data integrity and security risks.

Hence, Option C is the correct and Salesforce-approved environment for safe, realistic testing of agents.

References (AgentForce Documents / Study Guide):

AgentForce Testing Center Guide: “Supported Environments and Best Practices”

Salesforce Sandbox Management Guide

AgentForce Study Guide: “Safe Testing with Production-Replicated Data”

In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states: “AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context.”

This design ensures dynamic adaptability—the agent can choose the correct topic and associated action based on natural language understanding. Option A is incorrect because topic-to-utterance mapping is a configuration aid, not the selection mechanism. Option C is incorrect because the reasoning engine does not select actions by name—it interprets user intent via the LLM and executes mapped actions if relevant.

Therefore, Option B reflects the official operational flow of AgentForce’s LLM-driven reasoning process.

References (AgentForce Documents / Study Guide):

AgentForce Reasoning Engine Overview

AgentForce Builder User Guide: “Topic, Action, and LLM Selection Flow”

AgentForce Study Guide: “How the LLM Chooses Topics and Executes Actions”

Context of the Question

Universal Containers (UC) wants to build a Flex prompt template that uses data returned by a Flow.

“Flex Prompt Templates” allow admins and Agentforce Specialists to incorporate external or dynamic data into generative AI prompts.

Why “Add Flow Instructions” Is Needed

Passing Flow Data into Prompt Templates: When configuring the prompt, you must specify how data from the running Flow is passed into the Flex template. The designated element for that is typically “Flow Instructions,” which map the Flow outputs to the prompt.

Other Options:

Add Flex Instructions: Typically controls how the AI responds or structures the output, not how to bring Flow data into the template.

Add Prompt Instructions: Usually for static or manual instructions that shape the AI’s response, rather than referencing dynamic data from the Flow.

Outcome

“Add Flow Instructions” ensures the prompt can dynamically use the data that the Flow returns—making Option C correct.

Salesforce Agentforce Specialist References & Documents

Salesforce Help & Training: Using Prompt Templates with FlowExplains how to pass Flow variables into a prompt template via a specialized step (e.g., “Flow Instructions”).

Salesforce Agentforce Specialist Study GuideOutlines how to configure generative AI prompts that reference real-time Flow data.

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the AgentForce Orchestration and Action Sequencing Guidelines in the official documentation, deterministic execution order is best achieved by using custom state variables and conditional logic rather than relying solely on LLM reasoning or topic instructions.

AgentForce’s orchestration framework allows developers to define variables that represent the successful completion of specific actions (e.g., “sessionsRetrieved,” “eligibilityVerified,” etc.). Subsequent actions can then include conditional filters that only allow execution if prior steps have been completed. This approach ensures that actions execute in a strict, logical sequence — preventing the LLM from reordering steps arbitrarily.

Option A (relying on topic instructions) provides guidance to the LLM but does not enforce execution order programmatically, which means errors can still occur if the reasoning engine interprets steps differently. Option C (priority and sequence indicators) assists in contextual selection but does not create dependency-based control between actions.

Therefore, per AgentForce best practices, the correct approach is Option B – using custom variables with conditional filters. This guarantees deterministic workflow sequencing, prevents premature action execution, and aligns with the “Action Dependency and Conditional Execution Model” described in the AgentForce Implementation Guide.

UC’s development team needs to identify a core component of a Custom Agent Action in Agent Builder. Let’s assess the options.

Option A: Action Triggers"Action Triggers" isn’t a term used in Agentforce Custom Agent Action configuration. Actions are invoked by topics or plans, not standalone triggers, making this incorrect.

Option B: InstructionsInstructions are a core component of a Custom Agent Action in Agentforce. Defined in Agent Builder, they guide the Atlas Reasoning Engine on how to execute the action (e.g., what to do with inputs, how to process data). Reviewing the instructions helps the team understand the action’s purpose and logic, making this the correct answer.

Option C: Output TypesWhile outputs are part of an action’s result, "Output Types" isn’t a distinct configuration element in Agent Builder. Outputs are determined by the action’s execution (e.g., Flow or Apex), not a separate setting, making this less core and incorrect.

Why Option B is Correct:

Instructions are a fundamental component of Custom Agent Actions, providing the AI’s execution directives, as per Salesforce documentation.

According to the AgentForce for Service Configuration Guide, when implementing Service Agents on Email, administrators must create an Email Configuration to connect the agent with the appropriate email channel and templates. The documentation specifies: “To enable Service Agents to handle emails, create an Email Configuration that links the agent to the email address, template, and routing parameters. This configuration allows the Service Agent to read, interpret, and respond using the defined template.”

Option B (creating an Omni-Channel flow) applies to routing live messages or chats, not configuring email agents.

Option C is incorrect because Service Agents do not automatically connect to email templates — a manual configuration is required.

Thus, Option A is correct as it aligns with Salesforce’s documented process for connecting email templates to AgentForce Service Agents.

References (AgentForce Documents / Study Guide):

AgentForce for Service Setup Guide: “Creating Email Configurations for Service Agents”

Salesforce Service Cloud Email Configuration Overview

AgentForce Study Guide: “Deploying Service Agents on Email Channels”

The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.

Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.

Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.

Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.