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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types |
| Topic 2: Development Lifecycle | 15% | - Version control and maintenance - Deployment: sandbox to production - Testing agents in Testing Center - Monitoring, adoption and optimization |
| Topic 3: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Topic 4: Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt Builder usage and use cases - Prompt management and user roles - Prompt template creation: field generation, flex types |
| Topic 5: AI Agents | 35% | - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine - Topics and actions configuration - Deterministic controls: filters and variables - Agent User security and permissions |
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NEW QUESTION # 244
Universal Containers (UC) has a library of custom-built personalized investment portfolio APIs, and is planning to extend it to agents. Which method should UC's agent choose to dynamically use the best API service?
Answer: A
Explanation:
The most appropriate and advanced method for an Agentforce agent to dynamically select and use the best API service from a library of custom-built APIs is through Model Context Protocol (MCP) server support (B).
The Model Context Protocol (MCP) is an open standard specifically designed to standardize how AI agents and Large Language Models (LLMs) interact with external tools, systems, and data sources (like custom APIs). An external system, such as a server hosting UC's custom portfolio APIs, can be exposed as an MCP Server. This server provides rich, standardized, human- readable metadata about its "tools" (the APIs it offers). The Agentforce Atlas Reasoning Engine can interpret this metadata to understand the function of each API, the required inputs, and the expected outputs. This allows the agent to dynamically discover, reason over, and select the most appropriate API to execute based on a user's request (e.g., "Show me the best-performing portfolio" vs. "Adjust my risk tolerance").
NEW QUESTION # 245
Universal Containers (UC) is tracking web activities in Data Cloud for a unified contact, and wants to use that in a prompt template to help extract insights from the data. Assuming that the Contact object is one of the objects associated with the prompt template, what is a valid way for DC to do this?
Answer: B
Explanation:
To integrate web activity data from Data Cloud into a prompt template, the correct approach is to enrich the Contact object with the activity records as a related list and use related list grounding (Option B). Here's why:
Data Cloud Integration: Data Cloud unifies web activity data and associates it with the unified Contact record. By adding these activities as a related list to the Contact, the data becomes accessible to the prompt template.
Prompt Template Grounding: Salesforce prompt templates support grounding on related records.
When the Contact is passed to the prompt template, the template can reference the related web activity records (via the related list) to extract insights.
Structured Data Handling: This method aligns with Salesforce best practices for grounding, ensuring the large language model (LLM) receives structured, context-rich data without overwhelming it with raw activity lists.
NEW QUESTION # 246
Leadership needs to populate a dynamic form field with a summary or description created by a large language model (LLM) to facilitate more productive conversations with customers.
Leadership also wants to keep a human in the loop to be considered in their AI strategy. Which prompt template type should the Agentforce Specialist recommend?
Answer: B
Explanation:
Why is "Field Generation" the correct answer?
In Agentforce, the Field Generation prompt template type is designed to populate dynamic form fields with AI-generated content, such as summaries or descriptions created by a large language model (LLM).
Key Considerations for Using Field Generation in Dynamic Forms:
AI-Powered Summarization in Form Fields
Field Generation templates allow real-time AI-generated summaries based on customer data.
The summary is dynamically populated in the form field for the sales or service representative to review.
Human-in-the-Loop AI Strategy
Since leadership wants a human to be involved, Field Generation ensures the AI-generated content is editable before submission.
This keeps a human-in-the-loop, allowing manual review before finalizing responses.
Works with Salesforce Dynamic Forms
Field Generation templates integrate seamlessly with Salesforce Dynamic Forms, ensuring AI- powered insights are embedded within form layouts.
NEW QUESTION # 247
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?
Answer: A
Explanation:
When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data.
The Salesforce Data Cloud (formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI- powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC) can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.
NEW QUESTION # 248
What is the primary function of the planner service in the Agent system?
Answer: A
Explanation:
The primary function of the planner service in the Agent system is to identify copilot actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Agent should perform based on user input.
NEW QUESTION # 249
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