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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Topic 2: AI Agents | 35% | - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee - Agent User security and permissions - Deterministic controls: filters and variables - Topics and actions configuration |
| Topic 3: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices |
| Topic 4: Data Cloud for Agentforce | 20% | - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding |
| Topic 5: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
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NEW QUESTION # 215
Universal Containers has a new AI project. What should An Agentforce consider when adding a related list on the Account object to be used in the prompt template?
Answer: B
Explanation:
Universal Containers (UC) wants to include details from a related list on the Account object in a prompt template. This is typically done via Prompt Builder in Salesforce's generative AI setup.
Prompt Builder Behavior
Selecting a Related List: Within Prompt Builder, you can navigate to the object (Account) and choose which related list (e.g., Contacts, Opportunities) you want to reference.
Field Picker: Once a related list is chosen, Prompt Builder provides a field picker interface, allowing you to select specific fields from that related list. These fields then become available for merge fields or dynamic insertion within your prompt.
NEW QUESTION # 216
A Salesforce Administrator wants to generate personalized, targeted emails that incorporate customer interaction data. The admin wants to leverage large language models (LLMs) to write the emails, and wants to reuse templates for different products and customers. Which solution approach should the admin leverage?
Answer: B
Explanation:
To generate personalized emails using LLMs while reusing templates:
Sales Email Prompt Template Type (Option C): Designed specifically for generating dynamic email content by combining LLMs with structured templates. It allows admins to define placeholders (e.g., customer name, product details) and reuse templates across scenarios.
NEW QUESTION # 217
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 # 218
Universal Containers (UC) plans to implement prompt templates that utilize the standard foundation models. What should UC consider when building prompt templates in Prompt Builder?
Answer: A
Explanation:
UC is using Prompt Builder with standard foundation models (e.g., via Atlas Reasoning Engine).
NEW QUESTION # 219
How does Agentforce select the correct action to resolve a user's request?
Answer: B
Explanation:
In the AgentForce Architecture and Reasoning Engine Overview, Salesforce explains that the large language model (LLM) drives topic and action selection. The documentation states:
"AgentForce uses an LLM to interpret user intent, map it to existing topics, and trigger the appropriate action when available. If no matching topic or action is found, the LLM attempts to generate a direct response using its available context."
NEW QUESTION # 220
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