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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Unstructured data processing: chunking, indexing |
| Development Lifecycle | 15% | - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance - Monitoring, adoption and optimization |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types - Prompt management and user roles - Grounding techniques and best practices |
| AI Agents | 35% | - Agent User security and permissions - Topics and actions configuration - Deterministic controls: filters and variables - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack |
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NEW QUESTION # 287
Coral Cloud Resorts needs consistent pass/fail logic for agent testing. Which Testing Center capability provides that?
Answer: C
Explanation:
According to the AgentForce Testing Center Guide, structured batch testing is the feature that provides consistent, repeatable pass/fail validation for agent testing. The documentation explains:
"Structured batch testing allows specialists to define expected outputs per test utterance and automatically validate the agent's responses, resulting in deterministic pass/fail outcomes."
NEW QUESTION # 288
Universal Containers has set up a Service Agent to allow customers to look up their order status.
The topic setup includes:
Name: Order Inquiry
Classification Description: Handles user requests to look up order status, including tracking details and delivery estimates for orders placed within the last 90 days.
Scope: Your job is only to assist authenticated users in looking up the status of their orders placed within the last 90 days. If the order is pending delivery, provide the tracking number and estimated delivery date. Do not handle inquiries for orders older than 90 days.
Which information will be used by the Agentforce reasoning engine to choose this topic?
Answer: A
Explanation:
The AgentForce Reasoning Engine Guide explains that the engine relies primarily on the Classification Description and Scope fields to determine which topic best matches the user's intent. The documentation notes: "Classification Description defines the purpose and context of a topic, while Scope provides the operational boundaries for when and how that topic should be triggered. Together, they guide the LLM in selecting the appropriate topic at runtime."
NEW QUESTION # 289
An Agentforce is creating a custom action in Agent. Which option is available for the Agentforce Specialist to choose for the custom copilot action?
Answer: C
Explanation:
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.
NEW QUESTION # 290
Universal Containers wants to allow its service agents to query the current fulfillment status of an order with natural language. There is an existing autolaunched flow to query the Information from Oracle ERP, which is the system of record for the order fulfillment process. How should an Agentforce Specialist apply the power of conversational AI to this use case?
Answer: B
Explanation:
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.
NEW QUESTION # 291
Why is starting with a narrowly scoped Agentforce use case considered a best practice?
Answer: D
Explanation:
A narrow scope limits unintended behavior and makes monitoring, tuning, and governance easier.
NEW QUESTION # 292
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