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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 2: Einstein Trust Layer & Security | - Data Masking
| |
| Topic 3: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Topic 4: Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
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NEW QUESTION # 66
Universal Containers has grounded a prompt template with a related list. During user acceptance testing (UAT), users are not getting the correct responses. What is causing this issue?
Answer: C
Explanation:
UC has grounded a prompt template with a related list, but the responses are incorrect during UAT. Grounding with related lists in Agentforce allows the AI to access data from child records linked to a parent object.
NEW QUESTION # 67
Which business requirement presents a good use case for leveraging Einstein Prompt Builder?
Answer: C
Explanation:
Einstein Prompt Builder is a Salesforce feature that helps generate text (summaries, email content, responses) using AI models.
The question presents three potential use cases, asking which one best fits the capabilities of Einstein Prompt Builder.
Einstein Prompt Builder Typical Use Cases
Text Generation & Summaries: Great for writing or summarizing content, like responding to an email or generating text for a record field.
NEW QUESTION # 68
Universal Containers (UC) is preparing and defining success criteria for Agentforce Testing Center test cases. Which details should UC specify as the expected output to ensure the tests accurately reflect the agent's functionality?
Answer: B
Explanation:
According to the AgentForce Testing Center Reference Guide, each test case in the Testing Center should define a clear expected output to validate that the agent selects and executes the correct topic in response to a given user utterance.
The Expected Topic API Name acts as the validation reference -- it ensures that the reasoning engine correctly classifies the user's intent and routes the conversation to the appropriate topic.
This allows the test to confirm end-to-end functionality, from intent detection to action execution.
NEW QUESTION # 69
Which object stores the conversation transcript between the customer and the agent?
Answer: C
Explanation:
Why is "Messaging Session" the correct answer?
In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.
Key Features of the Messaging Session Object:
Stores the Entire Customer-Agent Conversation
The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.
This ensures that past interactions can be referenced during follow-ups.
Supports AI-Powered Work Summaries
Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.
These summaries are stored and accessible for later reference.
Links with Service Cloud for Case Resolution
If a conversation escalates into a case, the Messaging Session object can be linked to it.
This allows support teams to review the conversation history without switching contexts.
NEW QUESTION # 70
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?
Answer: A
Explanation:
The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: "Einstein Studio" may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context.
NEW QUESTION # 71
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