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| Section | Weight | Objectives |
|---|---|---|
| Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| AI Agents | 35% | - Agent types: Service, Sales, Employee - Topics and actions configuration - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent User security and permissions |
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Grounding techniques and best practices |
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NEW QUESTION # 28
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?
Answer: C
Explanation:
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses.
NEW QUESTION # 29
An Agentforce Specialist created a Field Generation prompt template. What should the Agentforce Specialist do to expose the template to the user?
Answer: B
Explanation:
The Field Generation prompt template type is specifically designed to enable generative AI within the context of a Salesforce record field. To expose this functionality to an end-user, the Agentforce Specialist must associate the template with the form field on the Lightning page (B).
This is accomplished using the Lightning App Builder:
The Agentforce Specialist first creates a custom field (often a Long Text Area or Rich Text Area) on the desired object to store the AI-generated output.
In the Lightning App Builder for the object's Record Page, the Specialist selects the field component.
In the properties panel for that field component, there is a setting (often a dropdown) to select an active Field Generation Prompt Template.
Once associated, an Einstein icon (or "Generate" button) appears next to the field on the record page, allowing the user to click it to run the prompt, review the AI-generated content, and then decide to use it to populate the field.
NEW QUESTION # 30
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system. Which data grounding technique should the Agentforce Specialist recommend?
Answer: B
Explanation:
Context of the Requirement:
Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
Data Grounding in LLM Prompts:
Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to use External Objects (via Salesforce Connect) when data resides outside of Salesforce.
NEW QUESTION # 31
Universal Containers has implemented an agent that answers questions based on Knowledge articles. Which topic and Agent Action will be shown in the Agent Builder?
Answer: A
Explanation:
UC's agent answers questions using Knowledge articles, configured in Agent Builder.
NEW QUESTION # 32
When creating a custom retriever in Einstein Studio, which step is considered essential?
Answer: B
Explanation:
In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
NEW QUESTION # 33
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