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| Section | Weight | Objectives |
|---|---|---|
| Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Reasoning Engine & AI Actions | - Workflow mastery
| |
| Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
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NEW QUESTION # 122
Universal Containers wants to implement a solution in Salesforce with a custom UX that allows users to enter a sales order number. Subsequently, the system will invoke a custom prompt template to create and display a summary of the sales order header and sales order details.
Which solution should an Agentforce Specialist implement to meet this requirement?
Answer: C
Explanation:
Universal Containers (UC) requires a solution with a custom UX for users to input a sales order number, followed by invoking a custom prompt template to generate and display a summary.
Let's evaluate each option based on this requirement and Salesforce Agentforce capabilities.
NEW QUESTION # 123
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: C
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 # 124
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 # 125
Universal Containers (UC) is implementing Agentforce Service Agent on Email, UC made an email template and now needs to connect it to a Service Agent. What should an Agentforce Specialist recommend?
Answer: B
Explanation:
According to the AgentForce for Service Configuration Guide, when implementing Service Agents on Email, administrators must create an Email Configuration to connect the agent with the appropriate email channel and templates. The documentation specifies: "To enable Service Agents to handle emails, create an Email Configuration that links the agent to the email address, template, and routing parameters. This configuration allows the Service Agent to read, interpret, and respond using the defined template."
NEW QUESTION # 126
After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?
Answer: B
Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
Low Temperature (e.g., 0.2): More deterministic, consistent responses.
High Temperature (e.g., 1.0): More creative, varied responses.
NEW QUESTION # 127
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