Agentforce-Specialist Prüfungsinformationen & Agentforce-Specialist Fragen Antworten

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Salesforce Agentforce-Specialist Prüfungsplan:

Thema	Einzelheiten
Thema 1	Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
Thema 2	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.
Thema 3	Multi-Agent Interoperability: This domain explains Model Context Protocol (MCP), agent-to-agent communication, and when to use Agent API for system interactions.
Thema 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.
Thema 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.

>> Agentforce-Specialist Prüfungsinformationen <<

Agentforce-Specialist Prüfungsfragen Prüfungsvorbereitungen 2026: Salesforce Certified Agentforce Specialist (AI-201) - Zertifizierungsprüfung Salesforce Agentforce-Specialist in Deutsch Englisch pdf downloaden

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Salesforce Certified Agentforce Specialist (AI-201) Agentforce-Specialist Prüfungsfragen mit Lösungen (Q114-Q119):

114. Frage
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. Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.
C. Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.

Antwort: C

Begründung:
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)

115. Frage
Universal Containers deploys a new Agentforce Service Agent into the company's website but is getting feedback that the Agentforce Service Agent is not providing answers to customer questions that are found in the company's Salesforce Knowledge articles. What is the likely issue?

A. The Agentforce Service Agent user was not given the Allow View Knowledge permission set.
B. The Agentforce Service Agent user is not assigned the correct Agent Type License.
C. The Agentforce Service Agent user needs to be created under the standard Agent Knowledge profile.

Antwort: A

Begründung:
Universal Containers (UC) has deployed an Agentforce Service Agent on its website, but it's failing to provide answers from Salesforce Knowledge articles. Let's troubleshoot the issue.
* Option A: The Agentforce Service Agent user is not assigned the correct Agent Type License.
There's no "Agent Type License" in Salesforce-agent functionality is tied to Agentforce licenses (e.g., Service Agent license) and permissions. Licensing affects feature access broadly, but the specific issue of not retrieving Knowledge suggests a permission problem, not a license type, making this incorrect.
* Option B: The Agentforce Service Agent user needs to be created under the standard Agent Knowledge profile.No "standard Agent Knowledge profile" exists. The Agentforce Service Agent runs under a system user (e.g., "Agentforce Agent User") with a custom profile or permission sets. Profile creation isn't the issue-access permissions are, making this incorrect.
* Option C: The Agentforce Service Agent user was not given the Allow View Knowledge permission set.The Agentforce Service Agent user requires read access to Knowledge articles to ground responses. The "Allow View Knowledge" permission (typically via the "Salesforce Knowledge User" license or a permission set like "Agentforce Service Permissions") enables this. If missing, the agent can't access Knowledge, even if articles are indexed, causing the reported failure. This is a common setup oversight and the likely issue, making it the correct answer.
Why Option C is Correct:
Lack of Knowledge access permissions for the Agentforce Service Agent user directly prevents retrieval of article content, aligning with the symptoms and Salesforce security requirements.
References:
Salesforce Agentforce Documentation: Service Agent Setup > Permissions - Requires Knowledge access.
Trailhead: Set Up Agentforce Service Agents - Lists "Allow View Knowledge" need.
Salesforce Help: Knowledge in Agentforce - Confirms permission necessity.

116. Frage
Universal Containers is building an Agentforce Service Agent to help customers track their purchases. To store the customer's order number during the conversation, the Agentforce Specialist creates and initializes a variable in Agent Script using the declaration order_id: string = " " , omitting the mutable keyword. During testing, the agent attempts to update this variable with the user's order number via the @utils.setVariables utility.
What is the result of this variable declaration at runtime?

A. The variable defaults to mutable; the mutable keyword is optional and has no effect, allowing the agent to successfully update the variable.
B. The variable is treated as read-only; it cannot be updated by actions or @utils.setVariables at runtime because it lacks the mutable keyword.
C. The declaration is a syntax error and the agent will fail to deploy.

Antwort: B

Begründung:
The correct answer is C because Agent Script variables are read-only unless they are explicitly declared with the mutable keyword. A regular variable without mutable can be referenced, but the agent cannot change its value during the conversation. That is exactly why order_id: string = " " is not suitable for capturing the customer's order number during runtime. To store information gathered from the user, the variable must be declared as a mutable string, such as order_id: mutable string = " " . Option A is wrong because mutability is not the default. Option B is wrong because the declaration itself is valid syntax; it simply creates a nonmutable variable. The runtime failure occurs when the agent or utility attempts to update it.

117. Frage
Universal Containers is building a custom agent and creating a new Apex action that accepts a collection of text values, such as a list of product names, as an input parameter. The Agentforce Specialist is configuring the action's metadata in Agentforce Assets and needs to properly map this input so the reasoning engine can pass the list of strings correctly.
When defining this input, which complex_data_type_name should the Agentforce Specialist use?

A. lightning__stringType
B. lightning__textType
C. apex__String

Antwort: A

Begründung:
The correct answer is A because Agentforce action metadata must use supported Lightning data types when defining action inputs and outputs. A collection of text values must be exposed to the reasoning engine through a Salesforce-supported Lightning type, not through an Apex implementation name.
lightning__stringType correctly maps string-based action input so Agentforce can validate, structure, and pass values into the Apex action. apex__String is not the correct metadata type because it describes an Apex language type, not the Agentforce action schema type. lightning__textType is a UI-oriented text type and is not the correct mapping for this collection-style string action input. Salesforce's Agentforce developer guidance confirms that Apex strings are mapped to standard Lightning types for action configuration.

118. Frage
What should Universal Containers consider when deploying an Agentforce Service Agent with multiple topics and Agent Actions to production?

A. 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.
B. Deploy flows or Apex after agents, topics, and Agent Actions to avoid deployment failures and potential production agent issues requiring complete redeployment.
C. Deploy agent components without a test run in staging, relying on production data for reliable results.
Sandbox configuration alone ensures seamless production deployment.

Antwort: A

Begründung:
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.

119. Frage
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