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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent User security and permissions - Topics and actions configuration - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Deterministic controls: filters and variables |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt management and user roles - Prompt template creation: field generation, flex types |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Version control and maintenance - Deployment: sandbox to production - Testing agents in Testing Center |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid |
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NEW QUESTION # 126
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
Answer: C
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs:
Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
NEW QUESTION # 127
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities. How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
Answer: B
Explanation:
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.
NEW QUESTION # 128
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past. What should UC consider when implementing this approach?
Answer: B
Explanation:
According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.
The documentation clearly explains:
"When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases." This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user's query.
NEW QUESTION # 129
An Agentforce is tasked to optimize a business process flow by assigning actions to agents within the Salesforce Agentforce Platform. What is the correct method for the Agentforce Specialist to assign actions to an Agent?
Answer: A
Explanation:
In the Salesforce Agentforce Platform, assigning actions to Agents requires mapping them through Topics in Agent Builder. Topics act as the container that links an Agent's conversational intent with the specific actions it can perform. By first assigning an action to a Topic in Agent Builder, the Agentforce Specialist ensures that the Agent knows when and how to trigger the action during the business process flow.
NEW QUESTION # 130
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?
Answer: C
Explanation:
According to the AgentForce Knowledge Integration and Access Configuration Guide, a Service Agent retrieves and grounds its responses using data from Salesforce Knowledge when the correct permissions are assigned. If customers report that the agent cannot access or provide answers from Knowledge articles, the most common root cause is that the AgentForce Service Agent user lacks the "Allow View Knowledge" permission.
This permission enables the agent to retrieve and read published articles from Salesforce Knowledge for grounding responses. Without it, the agent cannot access the content repository, resulting in incomplete or generic answers.
NEW QUESTION # 131
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