Free Practice Questions for Salesforce Agentforce-Specialist Exam

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.

UC is integrating an Agentforce agent with Omni-Channel Flow to route work. Let’s identify the correct element.

Option A: Route Work ActionThe "Route Work" action in Omni-Channel Flow assigns work items (e.g., cases, chats) to agents or queues based on routing rules. When connecting to an Agentforce agent, this action links the flow to the agent’s queue or presence, enabling interaction. This is the standard element for agent integration, making it the correct answer.

Option B: AssignmentThere’s no "Assignment" element in Flow Builder for Omni-Channel. Assignment rules exist separately, but within flows, routing is handled by "Route Work," making this incorrect.

Option C: DecisionThe "Decision" element branches logic, not connects to agents. It’s a control structure, not a routing mechanism, making it incorrect.

Why Option A is Correct:

"Route Work" is the designated Omni-Channel Flow action for connecting to agents, including Agentforce agents, per Salesforce documentation.

For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.

Option B is the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.

Option A (customer sentiment) is typically handled through NLP models, while Option C (product popularity) is more of a marketing analysis use case.

Salesforce Einstein Studio Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_overview

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)

The AgentForce Inter-Agent Communication and Security Configuration Guide specifies that when sensitive identity data (like a verified customer ID) must be shared between agents, the correct approach is to use the Agent API to initiate the downstream agent’s session. The verified data should be passed as a read-only context variable, ensuring persistence across sessions while preventing modification by the large language model (LLM).

This setup maintains data integrity and security compliance by isolating sensitive variables from the LLM’s reasoning layer. Context variables passed via the Agent API are immutable during runtime, ensuring they cannot be altered or exposed in agent-generated responses.

Option A adds unnecessary data persistence and complexity. Option B is insecure because conversation variables are exposed to the LLM context, risking unintended modification or leakage.

Therefore, the most secure and compliant configuration is Option C – Use the Agent API to pass verified IDs as read-only context variables between agents.

The Agentforce Specialist needs a comprehensive view of user interactions, errors, and action issues for troubleshooting. Let’s evaluate the options.

Option A: Plan CanvasPlan Canvas in Agent Builder visualizes an agent’s execution plan for a single interaction, useful for design but not for aggregated troubleshooting data like errors or all interactions, making it incorrect.

Option B: Agent SettingsAgent Settings configure the agent (e.g., topics, channels), not provide interaction logs or error details. This is for setup, not analysis, making it incorrect.

Option C: Event LogsEvent Logs in Agentforce (accessible via Setup or Agent Analytics) record all user interactions, including errors, incorrectly triggered actions, and incomplete plans. They provide detailed telemetry (e.g., timestamps, action outcomes) for troubleshooting performance issues, making this the correct answer.

Why Option C is Correct:

Event Logs offer the full scope of interaction data needed for troubleshooting, as per Salesforce documentation.

UC is using an Einstein Generative AI feature (likely Einstein Sales Emails) to draft personalized emails, but placeholders (e.g., {!Contact.FirstName}) appear instead of actual data from the contact record. Let’s analyze the options.

Option A: The user does not have permission to access the fields.Einstein Sales Emails, built on Prompt Builder, pulls data from contact records to populate email drafts. If the user lacks field-level security (FLS) or object-level permissions to access relevant fields (e.g., FirstName, Email), the system cannot retrieve the data, leaving placeholders unresolved. This is a common issue in Salesforce when permissions restrict data access, making it the most likely explanation and the correct answer.

Option B: The user’s locale language is not supported by Prompt Builder.Prompt Builder and Einstein Sales Emails support multiple languages, and locale mismatches typically affect formatting or translation, not data retrieval. Placeholders appearing instead of data isn’t a documented symptom of language support issues, making this unlikely and incorrect.

Option C: The user does not have Einstein Sales Emails permission assigned.The Einstein Sales Emails permission (part of the Einstein Generative AI license) enables the feature itself. If missing, users couldn’t generate drafts at all—not just see placeholders. Since drafts are being created, this permission is likely assigned, making this incorrect.

Why Option A is Correct:

Permission restrictions are a frequent cause of unresolved placeholders in Salesforce AI features, as the system respects FLS and sharing rules. This is well-documented in troubleshooting guides for Einstein Generative AI.

Universal Containers (UC) needs a solution that can automatically qualify and nurture thousands of new leads 24/7, provide accurate and consistent responses to prospects, schedule meetings for qualified leads, and allow sales representatives to focus on relationship building and closing deals. These needs align precisely with the Agentforce SDR Agent.

According to official AgentForce documentation, “Agentforce SDR helps sales teams qualify and nurture leads at scale, around the clock. It acts as a digital sales development representative capable of engaging new leads instantly, asking the right qualifying questions, answering inquiries accurately using connected Salesforce data, and automatically scheduling meetings on behalf of the sales team.”

The documentation further explains that the SDR Agent is designed to “personalize outreach, manage follow-up sequences, and book meetings directly from your website or campaign pages.” This automation “frees your human reps to focus on high-value interactions and closing opportunities rather than manual lead qualification.”

By contrast, the Sales Coach capability focuses on guiding and coaching sales representatives internally rather than interacting with prospects, and the Commerce Agent is designed for e-commerce use cases such as assisting shoppers with product discovery and order management—not lead nurturing.

References (AgentForce Documents / Study Guide):

AgentForce SDR Overview – Salesforce AgentForce Documentation

AgentForce for Sales – SDR Agent Use Cases

AgentForce Study Guide: “Qualify and Nurture Leads at Scale with SDR Agents”

Salesforce Trailhead: “Get to Know AgentForce SDR”

The Agentforce Specialist is defining a custom action for an Agentforce agent in Agent Builder. Actions determine what the agent does (e.g., retrieve data, update records). Let’s evaluate the options.

Option A: Apex TriggerApex Triggers are event-driven scripts, not selectable actions in Agent Builder. While Apex can be invoked via other means (e.g., Flows), it’s not a direct option for custom agent actions, making this incorrect.

Option B: SOQLSOQL (Salesforce Object Query Language) is a query language, not an executable action type in Agent Builder. While actions can use queries internally, SOQL isn’t a standalone option, making this incorrect.

Option C: FlowsIn Agentforce Studio’s Agent Builder, custom actions can be created using Salesforce Flows. Flows allow complex logic (e.g., data retrieval, updates, or integrations) and are explicitly supported as a custom action type. The specialist can select an existing Flow or create one, making this the correct answer.

Option D: JavaScriptJavaScript isn’t an option for defining agent actions in Agent Builder. It’s used in Lightning Web Components, not agent configuration, making this incorrect.

Why Option C is Correct:

Flows are a native, flexible option for custom actions in Agentforce, enabling tailored functionality for agents, as per official documentation.