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質問 # 181
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the Agentforce Specialist recommend to achieve this use case?
正解：C
解説：
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine- tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
Reference:
Salesforce Help Article: Prompt Builder for No-Code AI ("Grounding in CRM and Data Cloud" section).
Einstein GPT Implementation Guide: "Generating Summaries with Pre-Built Models."
質問 # 182
Universal Containers (UC) wants to make a sales proposal and directly use data from multiple unrelated objects (standard and custom) in a prompt template. How should UC accomplish this?
正解：D
解説：
UC needs to incorporate data from multiple unrelated objects (standard and custom) into a prompt template for a sales proposal. Let's evaluate the options based on Agentforce capabilities.
Option A: Create a prompt template passing in a special custom object that connects the records temporarily.
While a custom object could theoretically act as a junction to link unrelated records, this approach requires additional setup (e.g., creating the object, populating it with data via automation), and there's no direct mechanism in Prompt Builder to "pass in" such an object to a prompt template without grounding or flow support. This is inefficient and not a native feature, making it incorrect.
Option B: Create a prompt template-triggered flow to access the data from standard and custom objects.There' s no such thing as a "prompt template-triggered flow" in Salesforce. Flows can invoke prompt templates (e.g., via the "Prompt Template" action), but the reverse-triggering a flow from a prompt template-is not a standard construct. While a flow could gather data from unrelated objects and pass it to a prompt, this option' s terminology is inaccurate, and it's not the most direct solution, making it incorrect.
Option C: Create a Flex template to add resources with standard and custom objects as inputs.In Agentforce's Prompt Builder, a Flex template (short for Flexible Prompt Template) allows users to define dynamic inputs, including data from multiple Salesforce objects (standard or custom), even if they're unrelated. Resources can be added to the template (e.g., via merge fields or Data Cloud queries), enabling the prompt to pull data directly from specified objects without requiring a junction object or complex flows. This is ideal for generating a sales proposal using disparate data sources and aligns with Salesforce's documentation on Flex templates, making it the correct answer.
Why Option C is Correct:
Flex templates are designed for scenarios requiring flexible data inputs, allowing UC to directly reference multiple unrelated objects in the prompt template. This simplifies the process and leverages Prompt Builder's native capabilities, as outlined in Salesforce documentation.
References:
Salesforce Agentforce Documentation: Prompt Builder > Flex Templates - Describes adding multiple object resources as inputs.
Trailhead: Build Prompt Templates in Agentforce - Highlights Flex templates for dynamic data scenarios.
Salesforce Help: Create Flexible Prompts - Confirms support for standard and custom object data.
質問 # 183
Universal Containers (UC) wants to implement an AI-powered customer service agent that can:
* Retrieve proprietary policy documents that are stored as PDFs.
* Ensure responses are grounded in approved company data, not generic LLM knowledge.What should UC do first?
正解：A
解説：
Comprehensive and Detailed In-Depth Explanation:To implement an AI-powered customer service agent that retrieves proprietary policy documents (stored as PDFs) and ensures responses are grounded in approved company data, UC must first establish a foundation for the AI to access and use this data. TheAgentforce Data Library(Option A) is the correct starting point. A Data Library allows UC to upload PDFs containing policy documents, index them into Salesforce Data Cloud's vector database, and make them available for AI retrieval. This setup ensures the agent can perform Retrieval-Augmented Generation (RAG), grounding its responses in the specific, approved content from the PDFs rather than relying on generic LLM knowledge, directly meeting UC's requirements.
* Option B: Expanding the AI agent's scope to search all Salesforce records is too broad and unnecessary at this stage. The requirement focuses on PDFs with policy documents, not all Salesforce data (e.g., cases, accounts), making this premature and irrelevant as a first step.
* Option C: "Add the files to the content, and then select the data library option" is vague and not a precise process in Agentforce. While uploading files is part of setting up a Data Library, the phrasing suggests adding files to Salesforce Content (e.g., ContentDocument) without indexing, which doesn't enable AI retrieval. Setting up the Data Library (A) encompasses the full process correctly.
* Option A: This is the foundational step-creating a Data Library ensures the PDFs are uploaded, indexed, and retrievable by the agent, fulfilling both retrieval and grounding needs.
Option A is the correct first step for UC to achieve its goals.
References:
* Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help:https://help.salesforce.
com/s/articleView?id=sf.agentforce_data_library.htm&type=5)
* Salesforce Data Cloud Documentation: "Ground AI Responses with Data Cloud" (https://help.
salesforce.com/s/articleView?id=sf.data_cloud_agentforce.htm&type=5)
質問 # 184
Universal Containers is deploying two agents simultaneously: an internal Sales Productivity Agent for employees and a customer-facing Service Agent on its Experience Cloud site. An Agentforce Specialist is configuring permissions and needs to understand the correct security context for each.
Which statement accurately describes the execution model?
正解：C
解説：
The correct answer is B. Internal employee-facing agents operate within the authenticated Salesforce user's access model, so actions must respect the employee's profile, permission sets, sharing, and field- level security. A customer-facing Service Agent deployed through Experience Cloud is different: it is configured around an agent user/service agent execution identity, and Salesforce guidance stresses assigning the correct permissions to that agent user rather than relying on the site guest profile. Option A is wrong because a logged-in Experience Cloud deployment should not be treated as a guest-user execution model by default. Option C is also wrong because it incorrectly applies the dedicated Service Agent User model to the internal Sales Productivity Agent. The exam distinction is user-context execution for internal productivity versus dedicated service-agent execution for customer-facing service automation.
質問 # 185
What should Universal Containers consider when deploying an Agentforce Service Agent with multiple topics and Agent Actions to production?
正解：B
解説：
UC is deploying an Agentforce Service Agent with multiple topics and actions to production. Let's assess deployment considerations.
Option A: Deploy agent components without a test run in staging, relying on production data for reliable results. Sandbox configuration alone ensures seamless production deployment.Skipping staging tests is risky and against best practices. Sandbox configuration doesn't guarantee production success without validation, making this incorrect.
Option B: Ensure all dependencies are included, Apex classes meet 75% test coverage, and configuration settings are aligned with production. Plan for version management and post-deployment activation.This is a comprehensive approach: dependencies (e.g., flows, Apex) must be deployed, Apex requires 75% coverage, and production settings (e.g., permissions, channels) must align. Version management tracks changes, and post-deployment activation ensures controlled rollout. This aligns with Salesforce deployment best practices for Agentforce, making it the correct answer.
Option C: Deploy flows or Apex after agents, topics, and Agent Actions to avoid deployment failures and potential production agent issues requiring complete redeployment.Deploying components separately risks failures (e.g., actions needing flows failing). All components should deploy together for consistency, making this incorrect.
Why Option B is Correct:
Option B covers all critical deployment considerations for a robust Agentforce rollout, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: Deploy Agents to Production - Lists dependencies and coverage.
Trailhead: Deploy Agentforce Agents - Emphasizes testing and activation planning.
Salesforce Help: Agentforce Deployment Best Practices - Confirms comprehensive approach.
質問 # 186
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ちなみに、Jpexam Agentforce-Specialistの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1GMs5SsR-OMma9CtjgsxpfO8XQ_hDn77Y