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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance |
| Topic 2: AI Agents | 35% | - Topics and actions configuration - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Agent types: Service, Sales, Employee - Agent User security and permissions - Agent architecture and reasoning engine |
| Topic 3: Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Model Context Protocol (MCP) - Agent API usage scenarios |
| Topic 4: Data Cloud for Agentforce | 20% | - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid - Improving response accuracy with data grounding |
| Topic 5: Prompt Engineering | 20% | - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt management and user roles - Prompt Builder usage and use cases |
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NEW QUESTION # 117
Coral Cloud Resorts is uploading thousands of new HTML knowledge articles files for a resort launch. To ensure Agentforce retrieves accurate responses quickly, which chunking strategy should be used when creating a new index?
Answer: C
Explanation:
In AgentForce documentation on Knowledge Indexing and Chunking Strategies, Salesforce emphasizes that when uploading large volumes of structured content such as HTML or documentation files, the system should use section-aware chunking. The guide states: "Section- aware chunking preserves the logical boundaries of headings, paragraphs, and sub-sections in structured documents like HTML or PDF files, allowing the agent to retrieve contextually accurate and relevant responses quickly." This method ensures that the agent does not split content mid-section or lose contextual relationships between headings and body text. It enhances both retrieval speed and answer precision.
NEW QUESTION # 118
An Agentforce agent must retrieve data from Salesforce and an external system. What is the recommended approach?
Answer: D
Explanation:
External data access should be handled via Apex or Flow integrations, not direct LLM training.
NEW QUESTION # 119
An Agentforce Specialist created a Field Generation prompt template. What should the Agentforce Specialist do to expose the template to the user?
Answer: C
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 # 120
Universal Containers (UC) plans to automatically populate the Description field on the Account object. Which type of prompt template should UC use?
Answer: C
Explanation:
Universal Containers (UC) wants to automatically populate the Description field on the Account object. The AI-driven solution must generate textual data and write it directly into a field.
Field Generation Prompt Template
Primary Use Case: A Field Generation prompt template is specifically designed to create or fill in fields on a record with AI-generated text.
Auto-population: By configuring a Field Generation prompt template, admins can define the instructions, data inputs, and desired output for the AI. The resulting text then populates the specified field, such as the Account Description.
NEW QUESTION # 121
When creating a custom retriever in Einstein Studio, which step is considered essential?
Answer: C
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 # 122
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