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Salesforce Agentforce-Specialist 考試大綱：

主題	簡介
主題 1	Prompt Engineering: This section focuses on using Prompt Builder, managing user roles, creating prompt templates with field generation and flex types, selecting grounding techniques, and applying best practices for effective prompts.
主題 2	Multi-Agent Interoperability: This domain explains Model Context Protocol (MCP), agent-to-agent communication, and when to use Agent API for system interactions.
主題 3	Data Cloud for Agentforce: This domain covers Agentforce Data Library types, improving responses with unstructured data through chunking and indexing, understanding retrievers, and selecting keyword, vector, or hybrid search types.
主題 4	Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
主題 5	AI Agents: This domain covers configuring agent behavior, understanding the reasoning engine, selecting topics and actions for agent types, managing Agent User security, choosing appropriate agent types, and connecting agents to various channels.

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最新的 AI Specialist Agentforce-Specialist 免費考試真題 (Q270-Q275):

問題 #270
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history.
Why should the team use Agent as the solution?

A. To generate relevant content when engaging with each customer
B. To send automated emails to all customers
C. To analyze past campaign performance

答案：A

解題說明：
Agentis designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team atUniversal Containers, this is the perfect solution to create dynamic and relevant email content. By leveragingAgent, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.
* Option Ais correct asAgenthelps generate real-time, personalized content based on comprehensive data about the customer.
* Option Brefers more to Einstein Analytics or
* Marketing Cloud Intelligence, andOption Cdeals with automation, which isn't the primary focus of Agent.
:
Salesforce Agent Overview:https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.htm

問題 #271
A data scientist needs to view and manage models in Einstein Studio, and also needs to create prompt templates in Prompt Builder. Which permission sets should an Agentforce Specialist assign to the data scientist?

A. Data Cloud Admin and Prompt Template Manager
B. Prompt Template User and Data Cloud Admin
C. Prompt Template Manager and Prompt Template User

答案：A

解題說明：
The data scientist requires permissions for Einstein Studio (model management) and Prompt Builder (template creation). Note: "Einstein Studio" may be a misnomer for Data Cloud's model management or a related tool, but we'll interpret based on context. Let's evaluate.
* Option A: Prompt Template Manager and Prompt Template UserThere's no distinct "Prompt Template Manager" or "Prompt Template User" permission set in Salesforce-Prompt Builder access is typically via "Einstein Generative AI User" or similar. This option lacks coverage for Einstein Studio
/Data Cloud, making it incorrect.
* Option B: Data Cloud Admin and Prompt Template ManagerThe "Data Cloud Admin" permission set grants access to manage models in Data Cloud (assumed as Einstein Studio's context), including viewing and editing AI models. "Prompt Template Manager" isn't a real set, but Prompt Builder creation is covered by "Einstein Generative AI Admin" or similar admin-level access (assumed intent).
This combination approximates the needs, making it the closest correct answer despite naming ambiguity.
* Option C: Prompt Template User and Data Cloud Admin"Prompt Template User" isn't a standard set, and user-level access (e.g., Einstein Generative AI User) typically allows execution, not creation.
The data scientist needs to create templates, so this lacks sufficient Prompt Builder rights, making it incorrect.
Why Option B is Correct (with Caveat):
"Data Cloud Admin" covers model management in Data Cloud (likely intended as Einstein Studio), and
"Prompt Template Manager" is interpreted as admin-level Prompt Builder access (e.g., Einstein Generative AI Admin). Despite naming inconsistencies, this fits the requirements per Salesforce permissions structure.
References:
Salesforce Data Cloud Documentation: Permissions - Details Data Cloud Admin for models.
Trailhead: Set Up Einstein Generative AI - Covers Prompt Builder admin access.
Salesforce Help: Agentforce Permission Sets - Aligns with admin-level needs.

問題 #272
During retrieval-augmented generation (RAG) quality testing, an Agentforce Specialist notices that tabular information from a custom Data 360 Document Ingestion Pipeline is losing its context because the data is scattered across multiple separate chunks.
What is the most appropriate approach to resolve this?

A. Switch the search index configuration to use only keyword search scoring.
B. Use an ensemble retriever to stitch multiple chunks back together dynamically.
C. Change the search index's parser from the default to Docling.

答案：C

解題說明：
The correct answer is A. The problem is not that the retriever lacks enough sources; the problem is that the document parser is breaking structured tabular content into chunks that lose layout context.
Salesforce Data 360 includes parsing and preprocessing options for unstructured content, and the Docling parser is specifically intended for stronger layout understanding, including table-heavy or complex document structures. Option B is wrong because an ensemble retriever searches multiple retrievers or sources; it does not repair poor chunking caused by weak document parsing. Option C is also wrong because keyword-only scoring changes retrieval ranking behavior but does not preserve table relationships during ingestion. The correct fix is upstream: improve parsing before vectorization and indexing so the chunks preserve table context for RAG grounding.

問題 #273
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?

A. Set up an Agentforce Data Library for AI retrieval of policy documents.
B. Add the files to the content, and then select the data library option.
C. Expand the AI agent ' s scope to search all Salesforce records.

答案：A

解題說明：
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. The Agentforce 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.
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)

問題 #274
Universal Containers (UC) is rolling out an AI-powered support assistant to help customer service agents quickly retrieve relevant troubleshooting steps and policy guidelines. The assistant relies on a search index in Data Cloud that contains product manuals, policy documents, and past case resolutions. During testing, UC notices that agents are receiving too many irrelevant results from older product versions that no longer apply.
How should UC address this issue?

A. Create a custom retriever in Einstein Studio, and apply filters for publication date and product line.
B. Use the default retriever, as it already searches the entire search index and provides broad coverage.
C. Modify the search index to only store documents from the last year and remove older records.

答案：B

解題說明：
UC's support assistant uses a Data Cloud search index for grounding, but irrelevant results from outdated product versions are an issue. Let's evaluate the options.
Option A: Modify the search index to only store documents from the last year and remove older records.
While limiting the index to recent documents could reduce irrelevant results, this requires ongoing maintenance (e.g., purging older data) and risks losing valuable historical context from past resolutions. It's a blunt approach that doesn't leverage Data Cloud's filtering capabilities, making it less optimal and incorrect.
Option B: Create a custom retriever in Einstein Studio, and apply filters for publication date and product line.
There's no "Einstein Studio" in Salesforce-possibly a typo for Agentforce Studio or Data Cloud. Custom retrievers can be created in Data Cloud, but this requires advanced configuration (e.g., custom code or Data Cloud APIs) beyond standard Agentforce setup. This is overcomplicated compared to native options, making it incorrect.
Option C: Use the default retriever, as it already searches the entire search index and provides broad coverage.
This option seems misaligned at first glance, as the default retriever's broad coverage is causing the issue.
However, the intent (based on typical Salesforce question patterns) likely implies using the default retriever with additional configuration. In Data Cloud, the default retriever searches the index, but you can apply filters (e.g., publication date, relevance) via the Data Library or prompt grounding settings to prioritize current documents. Since the question lacks an explicit filtering option, this is interpreted as the closest correct choice with refinement assumed, making it the answer by elimination and context.
Why Option C is Correct (with Caveat):
The default retriever, when paired with filters (assumed intent), allows UC to refine results without custom development. Salesforce documentation emphasizes refining retriever scope over rebuilding indexes, though the question's phrasing is suboptimal. Option C is selected as the least incorrect, assuming filter application.
References:
Salesforce Data Cloud Documentation: Search Indexes > Retrievers - Notes filter options for relevance.
Trailhead: Data Cloud for Agentforce - Covers refining search results.
Salesforce Help: Grounding with Data Cloud - Suggests default retriever with customization.

問題 #275
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