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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent setup and orchestration
|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Prompt Engineering | 20% | - Prompt Builder usage
|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Governance & Observability | 10% | - AI governance
|
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NEW QUESTION # 186
Universal Containers (UC) needs to create a custom prompt template that can be called from a Lightning web component. Which prompt template type should UC create?
Answer: A
Explanation:
The AgentForce Developer Integration Guide specifies that Flex prompt templates are the correct type for custom or embedded integrations, such as invoking a prompt from a Lightning Web Component (LWC).
Flex templates are designed for general-purpose use cases and can be called programmatically via Apex, Flow, or LWC APIs. They offer flexible input and output structures, allowing developers to integrate AgentForce reasoning into custom applications and UI components.
NEW QUESTION # 187
Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent. Which channel consideration should UC be aware of while implementing it?
Answer: C
Explanation:
Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment.
NEW QUESTION # 188
Based on the user utterance, 'Show me all the customers in New York', which standard Agent action will the planner service use?
Answer: A
Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them. When a user issues a command like:
"Show me all the customers in New York", the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
Retrieves Data Based on Specific Field Values
This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
Uses standard or custom object fields (e.g., BillingState = 'New York').
Works with Large Language Models (LLMs) and Copilot Actions
When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
Optimized for Structured Data Retrieval
Ensures AI retrieves relevant CRM records quickly and accurately.
NEW QUESTION # 189
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?
Answer: A
Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
NEW QUESTION # 190
Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not be applicable for Einstein AI to draft accurate email responses. Which solution should UC use to ensure Einstein AI can draft responses from a defined data source?
Answer: B
Explanation:
Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or cases.
Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.
Work Summaries and Service Replies are useful features but do not address the need for grounding AI outputs in specific, current data sources like Service AI Grounding does.
For more details, you can refer to Salesforce's Service AI Grounding documentation for managing AI-generated content based on accurate data sources.
NEW QUESTION # 191
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