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

For Universal Containers (UC) to refine its Generative AI prompt design strategy and improve the accuracy of the generated summaries for the custom object Guest, the best practice is to focus on crafting concise, clear, and consistent prompt templates. This includes:

Effective grounding: Ensuring the prompt pulls data from the correct sources.

Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.

Clear instructions: Giving unambiguous directions on what to include in the response.

Iterative feedback: Regularly testing and adjusting prompts based on user feedback.

Option B is correct because it follows industry best practices for refining prompt design.

Option A (prompt test mode) is useful but less relevant for refining prompt design itself.

Option C (prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.

Salesforce Prompt Design Best Practices: https://help.salesforce.com/s/articleView?id=sf.prompt_design_best_practices.htm

Dynamic grounding with secure data retrieval is a key feature in Salesforce's Einstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allows relevant Salesforce data to be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.

Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.

The other options are less aligned with the requirement:

Data masking refers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.

Zero-data retention policy ensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

Salesforce Developer Documentation on Einstein Trust Layer

Salesforce Security Documentation for AI and Data Privacy

The scenario involves a customer service representative needing to cancel flights due to a weather alert and review existing Knowledge articles for guidance on canceling and rebooking. Agentforce provides capabilities to streamline such tasks. The most suitable option is Option B, which allows the agent to "execute tasks based on available actions" (e.g., canceling flights via a predefined action) while "answering questions using information from accessible Knowledge articles." This capability leverages Agentforce’s ability to integrate Knowledge articles into the agent’s responses, enabling the representative to ask questions (e.g., “How do I cancel a flight?”) and receive AI-generated answers grounded in approved Knowledge content. Simultaneously, the agent can trigger actions (e.g., a Flow to update the custom object) to perform the cancellations, meeting all requirements efficiently.

Option A: Invoking a Flow to call external data and create a Knowledge article is unnecessary. The representative needs to review existing articles, not create new ones, and there’s no indication external data is required for this task.

Option B: This is correct. It combines task execution (canceling flights) with Knowledge article retrieval, aligning with the representative’s need to act and seek guidance from existing content.

Option C: Generating a new Knowledge article based on prompts is not relevant. The representative needs to use existing articles, not author new ones, especially in a time-sensitive weather alert scenario.

Option B best supports the representative’s workflow in Agentforce.

Salesforce Agentforce Documentation: "Knowledge Replies and Actions" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm &type=5)

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

Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.

Supported Objects for Service AI Grounding:

Case

Knowledge

Case Object:

Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.

Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the specific customer issue being addressed.

Knowledge Object:

Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.

Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on verified information.

Exclusion of Other Objects:

Case Notes and Case Emails:

Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.

Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.

Why Options A and C are Incorrect:

Option A (Case, Knowledge, and Case Notes):

Case Notes Not Supported: Case Notes are not among the supported objects for grounding in Service AI.

Option C (Case, Case Emails, and Knowledge):

Case Emails Not Supported: Case Emails are also not included in the list of supported objects for grounding.

The AgentForce Security and Deterministic Logic Guide specifies that sensitive actions must be gated through conditional filters linked to verification variables, not through natural language. It states: “For any process requiring secure, deterministic access, create a custom variable (e.g., IsCustomerVerified) that stores the verification status as a Boolean. Apply a filter expression to all protected actions (e.g., IsCustomerVerified = true). This ensures the LLM cannot bypass or alter access logic.”

This configuration ensures security and determinism because the execution of sensitive actions is programmatically enforced, not dependent on the LLM’s understanding.

Option A is incorrect because natural language-based checks are non-deterministic.

Option B relies solely on topic instructions, which can be ignored or misinterpreted by the LLM.

Therefore, Option C is the only solution that provides deterministic, system-enforced access control.

References (AgentForce Documents / Study Guide):

AgentForce Security Configuration Guide: “Using Conditional Filters for Deterministic Access”

AgentForce Implementation Handbook: “Verification Variables and Secure Action Flow”

AgentForce Study Guide: “Protecting Sensitive Data in AI Workflows”

When creating a custom action in Agent, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the Agentforce Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code.

While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Agent because they are declarative and highly adaptable.

For further guidance, refer to Salesforce Flow documentation and Agent customization resources.

To add or update actions assigned to a copilot, An Agentforce can manage this through several areas:

Copilot Actions Page: This is the central location where copilot actions are managed and configured.

Record Page for the Copilot Action: From the record page, individual copilot actions can be updated or modified.

Copilot Action Library Tab: This tab serves as a repository where predefined or custom actions for Copilot can be accessed and modified.

These areas provide flexibility in managing and updating the actions assigned to Copilot, ensuring that the AI assistant remains aligned with business requirements and processes.

The other options are incorrect:

B misses the Copilot Action Library, which is crucial for managing actions.

C includes the Copilot Detail page, which isn't the primary place for action management.

Salesforce Documentation on Managing Copilot Actions

Salesforce Agentforce Specialist Guide on Copilot Action Management

Einstein Service Replies: This feature is designed to generate contextual, AI-powered response suggestions for agents during chat and email interactions. Crucially, these replies can be grounded in your organization's knowledge base, ensuring consistency and accuracy. This aligns perfectly with the requirement for quick, relevant, and consistent responses.

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​.

In Salesforce prompt templates, instructions that guide how the Large Language Model (LLM) should generate content (in this case, personalized emails) can be included by surrounding the instruction text with triple quotes ("""). This formatting ensures that the LLM adheres to the specific instructions while generating the email content.

The use of triple quotes allows the AI to understand that the enclosed text is a directive for how to approach the task, such as limiting the content to associated opportunity information or encouraging a specific action from the recipient.

Refer to Salesforce Prompt Builder documentation for detailed instructions on how to structure prompts for generative AI.

UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let’s evaluate.

Option A: Enable the agent to search all internal records and past customer inquiries.Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it incorrect.

Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval.The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.

Option C: Manually add policy responses into the AI model to prevent hallucinations.Manually embedding responses into the model isn’t feasible—Agentforce uses pretrained LLMs, not custom training. It also doesn’t support real-time updates, making this incorrect.

Why Option B is Correct:

The Data Library meets all criteria—semantic search, Knowledge grounding, and instant updates—per Salesforce’s recommended approach.

According to the AgentForce Solutions Overview and Employee Agent documentation, the Employee Agent is designed to empower internal teams, such as marketing, sales, HR, and operations, by providing AI-driven assistance within Salesforce. The guide explains: “Employee Agents help internal users find information, automate tasks, generate content, and manage projects directly in Salesforce. They surface personalized insights, support productivity, and leverage enterprise knowledge sources.”

In the given scenario, the marketing team requires capabilities for content creation, data lookup, and task management—all within Salesforce. This exactly matches the described functionality of an Employee Agent.

Option A (Sales Coach Agent) focuses on coaching sales representatives through role-play and performance feedback, while Option B (Service Agent) is built for customer support interactions. Thus, Option C is correct, as the Employee Agent fits the marketing productivity and knowledge use case described.

References (AgentForce Documents / Study Guide):

AgentForce Product Overview: “Employee Agent Capabilities”

AgentForce Study Guide: “Empowering Internal Teams with AI Agents”

AgentForce Implementation Guide: “Deploying Employee Agents for Marketing Use Cases”

Salesforce Agentforce leverages Prompt Builder, a powerful tool that allows administrators to create and manage prompt templates, which are reusable frameworks for generating AI-driven responses. These templates can be invoked by users to perform specific tasks, such as generating sales emails or summarizing records, based on predefined instructions and grounded data. In this scenario, Universal Containers wants its sales reps to have the ability to only execute these prompt templates, meaning they should be able to run them but not create, edit, or manage them.

Let’s break down the options and analyze why B. Prompt Template User permission set is the correct answer:

Option A: Prompt Execute Template permission setThis option sounds plausible at first glance because it includes the phrase "Execute Template," which aligns with the requirement. However, there is no specific permission set named "Prompt Execute Template" in Salesforce’s official documentation for Prompt Builder or Agentforce. Salesforce typically uses more standardized naming conventions for permission sets, and this appears to be a distractor option that doesn’t correspond to an actual feature. Permissions in Salesforce are granular, but they are grouped logically under broader permission sets rather than hyper-specific ones like this.

Option B: Prompt Template User permission setThis is the correct answer. In Salesforce, the Prompt Builder feature, which is integral to Agentforce, includes permission sets designed to control access to prompt templates. The "Prompt Template User" permission set is an official Salesforce permission set that grants users the ability to execute (or invoke) prompt templates without giving them the ability to create or modify them. This aligns perfectly with the requirement that sales reps should only execute prompt templates, not manage them. The Prompt Template User permission set typically includes permissions like "Run Prompt Templates," which allows users to trigger templates from interfaces such as Lightning record pages or flows, while restricting access to the Prompt Builder setup area where templates are designed.

Option C: Prompt Template Manager permission setThis option is incorrect because the "Prompt Template Manager" permission set is designed for users who need full administrative control over prompt templates. This includes creating, editing, and deleting templates in Prompt Builder, in addition to executing them. Since Universal Containers only wants sales reps to execute templates and not manage them, this permission set provides more access than required, violating the principle of least privilege—a key security best practice in Salesforce.

How It Works in Salesforce

To implement this, an administrator would:

Navigate to Setup > Permission Sets.

Locate or create the "Prompt Template User" permission set (this is a standard permission set available with Prompt Builder-enabled orgs).

Assign this permission set to the sales reps’ profiles or individual user records.

Ensure the prompt templates are configured and exposed (e.g., via Lightning components like the Einstein Summary component) on relevant pages, such as Opportunity or Account record pages, where sales reps can invoke them.

Why This Matters

By assigning the Prompt Template User permission set, Universal Containers ensures that sales reps can leverage AI-driven prompt templates to enhance productivity (e.g., drafting personalized emails or generating sales pitches) while maintaining governance over who can modify the templates. This separation of duties is critical in a secure Salesforce environment.

References to Official Salesforce Agentforce Specialist Documents

Salesforce Help: Prompt Builder PermissionsThe official Salesforce documentation outlines permission sets for Prompt Builder, including "Prompt Template User" for execution-only access and "Prompt Template Manager" for full control.

Trailhead: Configure Agentforce for ServiceThis module discusses how permissions are assigned to control Agentforce features, including prompt-related capabilities.

Salesforce Ben: Why Prompt Builder Is Vital in an Agentforce World (November 25, 2024)This resource explains how Prompt Builder integrates with Agentforce and highlights the use of permission sets like Prompt Template User to enable end-user functionality.

The AgentForce for Sales Overview defines Sales Coach as the AI-powered solution designed to help sales professionals practice and improve selling skills. The guide describes: “Sales Coach simulates customer interactions, allows reps to role-play objection handling, and provides personalized feedback based on real opportunity data.”

This directly matches the scenario where sales reps want to practice negotiation and objection handling with scenario-specific feedback.

Option A (Employee Coach) is intended for internal employee enablement and HR training use cases, not sales coaching.

Option B (SDR Agent) focuses on lead nurturing and prospecting, not sales training.

Therefore, Option C — Sales Coach — is the correct recommendation for simulation-based, personalized sales skill development.

References (AgentForce Documents / Study Guide):

AgentForce Sales Enablement Guide: “Sales Coach Overview and Capabilities”

AgentForce Product Documentation: “Practicing Sales Conversations with AI”

AgentForce Study Guide: “Simulated Coaching and Feedback for Sales Teams”

UC’s sales reps need an AI action to draft personalized emails based on past successful communications, reducing manual review time. Let’s evaluate the standard Agent actions.

Option A: Agent Action: Summarize Record"Summarize Record" generates a summary of a record (e.g., Opportunity, Contact), useful for overviews but not for drafting emails or leveraging past communications. This doesn’t meet the requirement, making it incorrect.

Option B: Agent Action: Find Similar Opportunities"Find Similar Opportunities" identifies past deals to inform strategy, not to draft emails. It provides data, not text generation, making it incorrect.

Option C: Agent Action: Draft or Revise Sales EmailThe "Draft or Revise Sales Email" action in Agentforce for Sales (sometimes styled as "Draft Sales Email") uses the Atlas Reasoning Engine to generate personalized email content. It can analyze past successful communications (e.g., via Opportunity or Contact history) to tailor emails for renewals or deals, saving reps time. This directly addresses UC’s need, making it the correct answer.

Why Option C is Correct:

"Draft or Revise Sales Email" is a standard action designed for personalized email generation based on historical data, aligning with UC’s productivity goal per Salesforce documentation.

As documented in the AgentForce Prompt Template and BYOLLM (Bring-Your-Own-LLM) Deployment Guide, each prompt template references a specific LLM configuration by name and ID. When migrating components between environments (e.g., from sandbox to production), the referenced LLM must also exist in the target org with the exact same name and identifier.

If the LLM configuration is missing or named differently in production, the deployment fails, as the prompt template cannot resolve its model dependency.

Option A is incorrect because specifying a custom LLM type does not resolve the missing configuration issue. Option B is incorrect because BYOLLM is supported in production, provided it is correctly registered.

Therefore, the error occurs because the LLM name or configuration ID does not match between sandbox and production, making Option C the correct answer.

Context of the Requirement:Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.

Data Grounding in LLM Prompts:Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to use External Objects (via Salesforce Connect) when data resides outside of Salesforce.

Why External Object Record Merge Fields:

External Objects appear much like standard or custom objects but map to tables in external systems.

You can reference fields from these External Objects in merge fields, allowing real-time data retrieval from the external ERP system without storing that data natively in Salesforce.

This is a simpler “point-and-reference” approach compared to coding custom Apex or configuring external services for direct prompt embedding.

Why Not External Services Merge Fields or Apex Merge Fields:

External Services Merge Fields typically leverage flows or external service definitions. While feasible, it is more about orchestrating or invoking external services for automation (e.g., Flow). It’s not the standard approach for seamlessly referencing external record data in prompt merges.

Apex Merge Fields would imply custom Apex code controlling the prompt insertion. While possible, it’s less “clicks not code” friendly and is not the default method for referencing typical record data.

References and Study Resources:

Salesforce Help & Training → Salesforce Connect and External Objects

Salesforce Trailhead → “Integrate External Data with Salesforce Connect”

Salesforce Agentforce Specialist Study Resources (documentation regarding how to ground LLM prompts using External Objects)

For sales reps who need to draft personalized emails based on previous communications, the Agentforce Specialist should recommend the Agent Action: Draft or Revise Sales Email. This action uses AI to generate or revise email content, leveraging past successful communications to create personalized and relevant outreach to prospects or clients.

Find Similar Opportunities is used for opportunity matching, not email drafting.

Summarize Record provides a summary of customer data but does not directly help with drafting emails.

For more information, refer to Salesforce's Agent documentation on standard actions for sales teams.