AI Specialist Agentforce-Specialist Syllabus Exam Questions Answers

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​.

Why is "Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI/Metadata API" the correct answer?

When deploying an Agentforce Agent with multiple topics and Agent Actions that use flows and Apex, a complete deployment solution is required. Change sets and the Salesforce CLI/Metadata API support the deployment of flows, Apex code, and agent-related metadata.

Key Considerations for Agentforce Deployments:

Supports Deployment of All Required Components

Agentforce Agents include flows, Apex classes, topics, and agent actions.

Change sets and Salesforce CLI/Metadata API allow deployment of all these components together, ensuring a smooth transition to production.

Agentforce Metadata Can Be Deployed Using Standard Tools

Change Sets: Allows admins to move configurations, custom objects, and metadata between Salesforce environments.

Salesforce CLI/Metadata API: Enables scripted deployments, automating the transfer of Agentforce configurations.

Ensures a Complete Migration Without Manual Configuration

Deploying all components together reduces the risk of misconfiguration.

Automating deployments using the Metadata API ensures consistency across environments.

Why Not the Other Options?

❌ A. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.

Incorrect because manually creating agent-related items in production introduces risk and inconsistency.

This approach is error-prone and time-consuming, especially for large Agentforce deployments.

❌ B. Use only change sets because the Salesforce CLI does not currently support the deployment of agent-related metadata.

Incorrect because Salesforce CLI and Metadata API fully support Agentforce deployments.

Change sets are useful but limited in large-scale, automated deployments.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that Agentforce metadata (flows, actions, and topics) can be deployed using Change Sets or the Metadata API​.

For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.

The AgentForce Data Cloud and Retrieval Configuration Guide explains that individual retrievers offer customization flexibility beyond the default retriever. The guide states: “Individual retrievers allow specialists to define filters, select specific fields for retrieval, and configure result limits, providing fine-grained control over data recall and relevance.”

Option A is incorrect because aggregation across multiple data spaces or DMOs is managed through composite retrievers, not individual retrievers.

Option C is also incorrect, as retrievers do not automatically generate or update indexes — indexing is handled separately within Data Cloud.

Therefore, Option B is correct since it represents the key advantage of individual retrievers: the ability to configure filters, fields, and retrieval parameters for precision control.

References (AgentForce Documents / Study Guide):

AgentForce Data Cloud Guide: “Individual vs. Default Retriever Configuration”

AgentForce Study Guide: “Fine-Tuning Retrieval Logic Using Individual Retrievers”

Einstein Studio for AgentForce: “Custom Filtering and Field Selection in Retrievers”