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Salesforce Agentforce-Specialist 考試大綱：

主題	簡介
主題 1	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 2	Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
主題 3	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 4	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.
主題 5	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.

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最新的 AI Specialist Agentforce-Specialist 免費考試真題 (Q352-Q357):

問題 #352
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?

A. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article. It is not possible to display articles that the LLM grounded its response in.
B. In the data library setting window, under Knowledge Settings, enable Use Public Knowledge Article and select Show sources,
C. Use Data Categories to categorize publicly available articles to index. Sources are automatically displayed when knowledge articles are categorized as Public.

答案：B

解題說明：
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable 'Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable 'Show Sources' so the agent links the specific articles or data records used to ground its response." Option A correctly reflects these two documented configuration steps.
Option B is incorrect because Salesforce explicitly supports source display for transparency through the
"Show Sources" setting.
Option C incorrectly assumes that Data Categories control indexing visibility and source linking, which is handled by explicit Knowledge Settings, not categorization.
References (AgentForce Documents / Study Guide):
* AgentForce Data Library Configuration Guide: "Knowledge Settings for Public Article Indexing"
* AgentForce Transparency and Source Attribution Notes: "Show Sources Option"
* AgentForce Study Guide: "Configuring Knowledge Visibility and Source Display"

問題 #353
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses.
UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?

A. Increase the maximum number of results returned to capture a broader dataset.
B. Define filters to narrow the search results based on specific conditions.
C. Change the search index to a different data model object (DMO).

答案：B

解題說明：
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, acustom retrieveris used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses.
UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is todefine filters(Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the 'Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.
* Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.
* Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.
* Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.
Option A is the most effective step to ensure relevance in retrieved data.
References:
* Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
* Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)

問題 #354
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities.
How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?

A. The LLM uses a static set of rules to match the user's request with predefined topics and actions, bypassing the need for dynamic interpretation and planning.
B. The LLM interprets the user's request, generates a plan by identifying the apcMopnete topics and actions, and executes the actions to retrieve and display the open opportunities
C. Using a dialog pattern. the LLM matches the user query to the available topic, action and steps thenperforms the steps for each action, such as retrieving a fast of open opportunities.

答案：B

解題說明：
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability.
Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.
Reference:
Salesforce Help Article: Agentforce Planner Service ("Dynamic Request Interpretation" section).
Einstein Agentforce Specialist Trailhead: "How Agentforce Processes User Requests."

問題 #355
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?

A. Safe
B. Moderately safe
C. Not safe

答案：A

解題說明：
In theEinstein Trust Layer, thetoxicity scoringsystem is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1indicates that the content is deemedsafe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by theEinstein Trust Layer.
For further reference, check Salesforce's officialEinstein Trust Layer documentationregardingtoxicity scoringfor AI-generated content.

問題 #356
When creating a custom retriever in Einstein Studio, which step is considered essential?

A. Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.
B. Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.
C. Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.

答案：B

解題說明：
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.
Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.
Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5) Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/einstein- studio-for-agentforce)

問題 #357
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