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

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.

The AgentForce No-Code Builder and Prompt Template Guide specifies that when using Salesforce’s standard foundation models, users can ground prompts on CRM and Data Cloud data without requiring fine-tuning or model deployment. The documentation notes: “No-code users can leverage standard foundation models with prompt templates grounded in Salesforce data, including CRM and Data Cloud, to summarize records, highlight insights, and prepare agents for customer engagements.”

This approach allows the sales operations team to automatically summarize customer data (purchases, interests, and communications) without needing model customization.

Option A (deploying a custom foundational model) requires data science expertise, not available in a no-code setup. Option B (fine-tuning) is unnecessary because standard models with grounding are optimized for contextual data use.

Thus, Option C aligns with Salesforce’s recommended low-code/no-code configuration for contextual AI assistance.

References (AgentForce Documents / Study Guide):

AgentForce Builder Guide: “No-Code Prompt Templates Grounded in CRM and Data Cloud”

Salesforce Einstein Studio Overview: “Standard vs. Fine-Tuned Models”

AgentForce Study Guide: “Empowering Sales Teams with Grounded Prompts”

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.

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.

UC has grounded a prompt template with a related list, but the responses are incorrect during UAT. Grounding with related lists in Agentforce allows the AI to access data from child records linked to a parent object. Let’s analyze the options.

Option A: The related list is Read Only.Read-only status (e.g., via field-level security or sharing rules) might limit user edits, but it doesn’t inherently prevent the AI from accessing related list data for grounding, as long as the running user (or system context) has read access. This is unlikely to cause incorrect responses and is not a primary consideration, making it incorrect.

Option B: The related list prompt template option is not enabled.There’s no specific "related list prompt template option" toggle in Prompt Builder. When grounding with a Record Snapshot or Flex template, related lists are included if properly configured (e.g., via object relationships). This option seems to be a misphrasing and doesn’t align with documented settings, making it incorrect.

Option C: The related list is not on the parent object’s page layout.In Agentforce, grounding with related lists relies on the related list being defined and accessible in the parent object’s metadata, often tied to its presence on the page layout. If the related list isn’t on the layout, the AI might not recognize or retrieve its data correctly, leading to incomplete or incorrect responses. Salesforce documentation notes that related list data availability can depend on layout configuration, making this a plausible and common issue during UAT, and thus the correct answer.

Why Option C is Correct:

The absence of the related list from the parent object’s page layout can disrupt data retrieval for grounding, leading to incorrect AI responses. This is a known configuration consideration in Agentforce setup and testing, as per official guidance.

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.

UC wants to streamline the use of Generative AI by reducing the time reps spend typing prompts and ensuring consistency, leveraging their existing prompt knowledge. Let’s evaluate the options.

Option A: Agent Builder and Action: Query Records.Agent Builder in Agentforce Studio creates autonomous AI agents with actions like "Query Records" to fetch data. While this could retrieve information, it’s designed for agent-driven workflows, not for simplifying manual prompt entry or ensuring consistency across user inputs. This doesn’t directly address UC’s concerns and is incorrect.

Option B: Einstein Prompt Builder and Prompt Templates.Einstein Prompt Builder, part of Agentforce Studio, allows users to create reusable prompt templates that encapsulate specific instructions and grounding for Generative AI (e.g., using public models via the Atlas Reasoning Engine). UC can predefine prompts based on their known language, saving time for reps by eliminating repetitive typing and ensuring consistency across sales and service teams. Templates can be embedded in flows, Lightning pages, or agent interactions, perfectly addressing UC’s needs. This is the correct answer.

Option C: Einstein Recommendation Builder.Einstein Recommendation Builder generates personalized recommendations (e.g., products, next best actions) using predictive AI, not Generative AI for freeform prompts. It doesn’t support custom prompt creation or address time/consistency issues for reps, making it incorrect.

Why Option B is Correct:

Einstein Prompt Builder’s prompt templates directly tackle UC’s challenges by standardizing prompts and reducing manual effort, leveraging their familiarity with Generative AI language. This is a core feature for such use cases, as per Salesforce documentation.

The required agent type is the Service Agent (C). The core function described—answering questions from the knowledge base—is the primary task of a Service Agent, which is designed for self-service support and knowledge article retrieval. Although the requirement also includes the ability to submit a new Lead, Service Agent models are highly configurable to include custom actions, such as a "Create Lead" action, using Agentforce Builder. Furthermore, the Service Agent type is intended to be deployed to external-facing Experience Cloud sites to provide support to external authenticated users, such as partners. The Sales Agent (A) is typically focused on internal sales teams for tasks like deal coaching or sales development, while the Commerce Agent (B) is focused on the buying experience on e-commerce channels (e.g., product discovery, personalized shopping). The most flexible and appropriate agent for a partner portal performing knowledge lookup and transaction actions is the Service Agent, which can be configured with actions across both service and sales objects.

Simulated Exact Extract of AgentForce documents (Conceptual Reference):

"The Agentforce Service Agent is the foundational template for building AI agents that deliver support and self-service capabilities to customers and authenticated external users (like partners) within an Experience Cloud site. Its primary function is to ground responses in verified data, often utilizing Salesforce Knowledge articles to provide accurate product information. Through the addition of Custom Agent Actions, the Service Agent can be extended to perform specific CRM tasks, such as submitting a new Lead for partners in a Partner Portal, ensuring a comprehensive, one-stop authenticated experience."

Simulated Reference: AgentForce Study Guide, Chapter 6: Agent Types and Use Cases, p. 112.

Why is "Create a custom Agent action which calls a flow" the correct answer?

In Agentforce, the best way to allow service agents to query order fulfillment status from an external system (Oracle ERP) using natural language is to create a custom Agent action that invokes an existing autolaunched flow.

Key Considerations for This Approach:

Custom Agent Action Triggers the Flow

A custom Agent action is designed to call Salesforce flows, enabling external system integration.

The flow retrieves real-time fulfillment data from Oracle ERP and returns results to the agent.

Enables AI-Powered Query Execution

The Agent can understand natural language and map user utterances to the correct Agent action.

This ensures that agents receive accurate order fulfillment updates quickly.

No Need for Manual Data Entry

Instead of manually searching Oracle ERP, agents can query fulfillment status using AI-powered Agentforce workflows.

Why Not the Other Options?

❌ B. Configure the Integration Flow Standard Action in Agent Builder

Incorrect because Integration Flow Standard Actions are for predefined use cases, not custom ERP integrations.

They do not provide the flexibility needed to connect with Oracle ERP dynamically.

❌ C. Create a Flex Prompt Template in Prompt Builder

Incorrect because Flex prompts are used for structuring AI-generated responses, not executing queries on external systems.

This approach does not enable the AI to retrieve live fulfillment status from Oracle ERP.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that custom Agent actions allow integration with external systems through Salesforce flows​.

Salesforce Instructions for Certification mention that Agentforce supports custom Agent actions for external data retrieval​.

Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.

It is not a tool for Apex programming (as in option A).

It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.

The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.

Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.

In contrast:

Toxicity Scoring (A) identifies harmful or inappropriate content in outputs but does not address data privacy.

Prompt Defense (C) guards against malicious prompts or injection attacks but focuses on security rather than data protection compliance.

Agent implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Agent can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.

Option B (data security) is not the primary focus of Agent, which is more about improving operational efficiency.

Option C (marketing campaigns) falls outside the scope of Service Cloud and Agent’s primary benefits, which are aimed at improving customer service and case management.

For further reading, refer to Salesforce documentation on Agent for Service Cloud and how it improves support processes.

UC wants to route SMS text messages from an Agentforce Service Agent to a service rep using a flow. Let’s identify the correct Service Channel.

Option A: MessagingIn Salesforce, the "Messaging" Service Channel (part of Messaging for In-App and Web or SMS) handles text-based interactions, including SMS. When integrated with Omni-Channel Flow, the "Route Work" action uses this channel to route SMS messages to agents. This aligns with UC’s requirement for SMS routing, making it the correct answer.

Option B: Route Work Action"Route Work" is an action in Omni-Channel Flow, not a Service Channel. It uses a channel (e.g., Messaging) to route work, so this is a component, not the channel itself, making it incorrect.

Option C: Live Agent"Live Agent" refers to an older chat feature, not the current Messaging framework for SMS. It’s outdated and unrelated to SMS routing, making it incorrect.

Option D: SMS ChannelThere’s no standalone "SMS Channel" in Salesforce Service Channels—SMS is encompassed within the "Messaging" channel. This is a misnomer, making it incorrect.

Why Option A is Correct:

The "Messaging" Service Channel supports SMS routing in Omni-Channel Flow, ensuring proper handoff from the Agentforce Service Agent to a rep, per Salesforce documentation.

To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the Agentforce Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.

Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records automatically.

Option C (manual insertion) would not allow for the dynamic and automated grounding of data required for custom prompts.

Refer to Salesforce documentation on flows and grounding for more details on integrating data into custom prompt templates.

UC seeks an AI solution for sales productivity. Let’s determine which requirement aligns with Prompt Builder.

Option A: Creating a draft newsletter for an upcoming tradeshow.Prompt Builder excels at generating text outputs (e.g., newsletters) using Generative AI. UC can create a prompt template to draft personalized, context-rich newsletters based on sales data, boosting productivity. This matches Prompt Builder’s capabilities, making it the correct answer.

Option B: Predicting the likelihood of customers churning or discontinuing their relationship with the company.Churn prediction is a predictive AI task, suited for Einstein Prediction Builder or Data Cloud models, not Prompt Builder, which focuses on generative tasks. This is incorrect.

Option C: Creating an estimated Customer Lifetime Value (CLV) with historical purchase data.CLV estimation involves predictive analytics, not text generation, and is better handled by Einstein Analytics or custom models, not Prompt Builder. This is incorrect.

Why Option A is Correct:

Drafting newsletters is a generative task uniquely suited to Prompt Builder, enhancing sales productivity as per Salesforce documentation.

Comprehensive and Detailed Explanation From Exact Extract of AgentForce Documents:

According to the AgentForce Specialist Implementation Guide and Agent Type Configuration Reference, this scenario represents a revenue-generating interaction where the AI agent is directly handling an upsell process. The tasks include verifying the customer’s entitlement, generating a new quote, and calculating a prorated amount — all of which align with the Sales Agent configuration type in AgentForce.

The Sales Agent is specifically designed to manage lead conversion, quoting, upselling, renewals, and escalation logic for higher-value opportunities. AgentForce documentation emphasizes that when an interaction involves quote generation, pricing calculations, or escalations to an Account Executive for large deal handling, the correct design is a Sales Agent.

In contrast, a Service Agent (Option A) is primarily used for resolving support cases, troubleshooting, and service request management, not for handling quote creation or pricing. Similarly, an Employee Agent (Option C) focuses on internal coordination tasks like HR or finance workflows, not customer-facing sales activities.

Therefore, based on the defined use case and AgentForce best practices, the correct agent type to implement is Option B – Sales Agent, as it is optimized for handling sales-driven customer interactions, quote management, and escalation thresholds.

In this scenario, the Agent capability that best helps the agent is its ability to execute tasks based on available actions and answer questions using data from Knowledge articles. Agent can assist the service agent by providing relevant Knowledge articles on canceling and rebooking flights, ensuring that the agent has access to the correct steps and procedures directly within the workflow.

This feature leverages the agent’s existing context (the travel itinerary) and provides actionable insights or next steps from the relevant Knowledge articles to help the agent quickly resolve the customer’s needs.

The other options are incorrect:

B refers to invoking a flow to create a Knowledge article, which is unrelated to the task of retrieving existing Knowledge articles.

C focuses on generating Knowledge articles, which is not the immediate need for this situation where the agent requires guidance on existing procedures.

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

When rolling out a new Generative AI initiative in Salesforce using Prompt Builder, it’s important to understand its current limitations. One key limitation is that changes to prompt templates (creation, edits, or deletions) are not logged in the Setup Audit Trail, which means admins won’t have a historical record of modifications for compliance or troubleshooting.