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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Topic 2: Prompt Engineering | 20% | - Prompt Builder usage
|
| Topic 3: Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Topic 4: AI Agents | 35% | - Agent behavior configuration
|
| Topic 5: Governance & Observability | 10% | - AI governance
|
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NEW QUESTION # 213
After a successful implementation of Agentforce Sates Agent with sales users. Universal Containers now aims to deploy it to the service team. Which key consideration should the Agentforce Specialist keep in mind for this deployment?
Answer: C
Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
NEW QUESTION # 214
An Agentforce Specialist needs to create a prompt template that extracts the customer's name, phone number, and case number from a block of text, and nothing else. How should the Agentforce Specialist structure the prompt to ensure the large language model (LLM) doesn't include extra conversation or text?
Answer: B
Explanation:
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well-defined output instructions and examples. The documentation specifies:
"To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key-value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language."
NEW QUESTION # 215
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management. For security purposes, UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed. Which approach best meets this requirement?
Answer: C
Explanation:
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable- based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example, `isVerified') that stores the result of an identity verification step.
Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals `true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.
NEW QUESTION # 216
Universal Containers (UC) wants to deploy an Agentforce Service Agent to support customers via a web experience. UC uses a Digital Experience site and wants to enable messaging for logged- in users. The customer needs to pass the membership number to the agent for which a pre-chat variable is available. What is a required step to connect the agent to the Digital Experience site using Messaging for In-App and Web?
Answer: A
Explanation:
The required step to route any messaging session, including those from Messaging for In-App and Web (MIAW) on a Digital Experience site, to an Agentforce Service Agent is to Create an Omni-Channel flow that routes messages to the agent (B).
Messaging for In-App and Web utilizes Omni-Channel Routing to direct incoming work items (which are created as MessagingSession records) to the correct destination. When setting up the Messaging Channel for a Digital Experience site, the Routing Type must be configured to use a Flow. This Flow is an Omni-Channel Flow that explicitly contains a Route Work action element configured to route to the Agentforce Service Agent.
Crucially, this Omni-Channel Flow also handles the passing of the pre-chat variables (like the membership number). The pre-chat variables are mapped in the Messaging Channel settings to input variables in the Omni-Channel Flow. The Flow can then use this membership number to perform a record lookup, enrich the context of the conversation (e.g., by updating the related MessagingSession record), and then successfully route the session to the Agentforce Service Agent using the Route To: Bot action, which targets the specific Agentforce Agent.
NEW QUESTION # 217
An Agentforce Specialist wants to ensure their custom agent action performs as expected in conversations. What should the Agentforce Specialist focus on when creating action instructions?
Answer: C
Explanation:
The AgentForce Action Design Guide emphasizes that concise and clear action instructions are essential for predictable and reliable agent behavior. The documentation states: "Action instructions should be concise, clearly define the purpose of the action, and specify its expected inputs and outputs. Each action must be validated through testing in AgentForce Builder to confirm the LLM interprets and executes it as intended."
NEW QUESTION # 218
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