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| Section | Weight | Objectives |
|---|---|---|
| Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt Builder usage and use cases - Grounding techniques and best practices - Prompt template creation: field generation, flex types |
| Development Lifecycle | 15% | - Monitoring, adoption and optimization - Deployment: sandbox to production - Testing agents in Testing Center - Version control and maintenance |
| AI Agents | 35% | - Topics and actions configuration - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent User security and permissions - Agent architecture and reasoning engine - Agent types: Service, Sales, Employee |
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid |
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NEW QUESTION # 158
What is the role of the large language model (LLM) in executing an Agent Action?
Answer: B
Explanation:
In Agent, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user's request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
NEW QUESTION # 159
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: B
Explanation:
UC's agent answers questions using Knowledge articles, configured in Agent Builder.
NEW QUESTION # 160
An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?
Answer: A
Explanation:
The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions.
NEW QUESTION # 161
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 # 162
Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. The goal is to enhance the team's performance by identifying areas for improvement and competitive intelligence. Which feature provides insights about competitor mentions and coaching opportunities?
Answer: B
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
NEW QUESTION # 163
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