Agentforce-Specialist復習解答例、Agentforce-Specialist最新テスト

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Salesforce Agentforce-Specialist 認定試験の出題範囲：

トピック出題範囲
トピック 1
  • Development Lifecycle: This area addresses testing agents in Testing Center, deploying from sandbox to production, and managing agent adoption and monitoring.
トピック 2
  • Multi-Agent Interoperability: This domain explains Model Context Protocol (MCP), agent-to-agent communication, and when to use Agent API for system interactions.
トピック 3
  • 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.
トピック 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.
トピック 5
  • 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.

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Salesforce Certified Agentforce Specialist (AI-201) 認定 Agentforce-Specialist 試験問題 (Q352-Q357):

質問 # 352
The support team at Coral Cloud Resorts needs to create a Flex prompt template that summarizes complex case histories for agent handoffs. The goal is to ensure summaries are concise and follow a specific three-part structure: Issue, Steps Taken, and Next Action.
What should an Agentforce Specialist recommend to ensure consistent data output?

正解：B

解説：
The correct answer is B because the requirement is about controlling the structure of generated text. In Prompt Builder, the cleanest way to obtain consistent output is to state the required format directly in the prompt instructions, including the exact section headings: Issue, Steps Taken, and Next Action. This gives the large language model a clear output contract and reduces variation between summaries.
Option A is wrong because chain-of-thought reasoning is not appropriate for exposing or forcing internal reasoning, and it does not guarantee a fixed business format. Option C is unnecessary because a flow can support orchestration, but the formatting requirement can be handled directly through explicit prompt instructions. Salesforce prompt engineering guidance emphasizes using clear task framing, examples, and structured instructions to improve output reliability.


質問 # 353
A business stakeholder wants to use Al to generate a summary based on Data Cloud data.
Which method(s) should the stakeholder use to access Data Cloud data from Prompt Builder?

正解：A

解説：
The Prompt Builder and Data Cloud Integration Guide explains that Data Cloud information can be accessed directly through Data Cloud related lists or prompt-initiated flows, which fetch relevant data dynamically. The documentation states: "Prompt Builder supports retrieving Data Cloud data using related lists for contextual grounding or invoking flows that query Data Cloud objects at runtime. This enables AI prompts to generate summaries, recommendations, or insights directly from unified customer profiles." Option A is incorrect because direct access to data model objects (DMOs) in Flex templates is not supported in Prompt Builder. Option C (external APIs) is unnecessary, as Prompt Builder has native integration with Data Cloud.
Thus, Option B is the correct and Salesforce-documented method to access Data Cloud data from Prompt Builder.
References (AgentForce Documents / Study Guide):
* Salesforce Prompt Builder Guide: "Integrating with Data Cloud"
* AgentForce Study Guide: "Fetching Data Cloud Data with Prompt-Initiated Flows"
* Salesforce Data Cloud Documentation: "Using Related Lists in Prompt Templates"


質問 # 354
Universal Containers is building a digital shopping assistant that needs to dynamically generate product recommendations using information from the company's external product recommendation predictive model through APIs.
Which Agentforce capability should make it easier for the agent to consume the external product recommendation tool?

正解：A

解説：
Per the AgentForce Integration and AI Architecture Guide, the Model Context Protocol (MCP) enables agents to interact dynamically with external predictive or AI models. The documentation states:
"Through MCP, agents can discover, connect, and invoke external models via standardized schema definitions. This allows agents to use third-party tools-like recommendation engines or classifiers-without pre-coding fixed API calls." Option A (MCP) supports dynamic interoperability with predictive models, making it the correct answer.
Option B (Hugging Face) refers to a model hosting platform, not a Salesforce integration mechanism.
Option C (A2A protocol) supports agent-to-agent communication, not external model invocation.
Therefore, Option A correctly reflects the Salesforce-recommended method for integrating external predictive APIs.
References (AgentForce Documents / Study Guide):
AgentForce Architecture Guide: "Integrating Predictive Models with MCP" AgentForce Technical Overview: "Model Context Protocol for External Tools" AgentForce Study Guide: "Using MCP for Recommendation and AI Model Access"


質問 # 355
Before activating a custom copilot action, An Agentforce would like is to understand multiple real-world user utterances to ensure the action being selected appropriately.
Which tool should theAgentforce Specialistrecommend?

正解：B

解説：
To understand multiple real-world user utterances and ensure the correct action is selected before activating a custom copilot action, the recommended tool isCopilot Builder. This tool allowsAgentforce Specialists to design and test conversational actions in response to user inputs, helping ensure the copilot can accurately handle different user queries and phrases.Copilot Builderprovides the ability to test, refine, and improve actions based on real-world utterances.
* Option Cis correct asCopilot Builderis designed for configuring and testing conversational actions.
* Option A(Model Playground) is used for testing models, not user utterances.
* Option B(Einstein Copilot) refers to the conversational interface but isn't the right tool for designing and testing actions.
References:
* Salesforce Copilot Builder Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_copilot_builder.htm


質問 # 356
Universal Containers has been building an agent using Canvas. It has created an "Issue Refund" action.
Business rules dictate that refunds can only be issued if the customer's Account Tier is "Platinum." Currently, the agent relies on its system instructions to check the tier, but it occasionally hallucinates authorization and attempts to call the refund action for Standard customers, resulting in backend errors.
How should the Agentforce Specialist use filters to deterministically ensure the agent cannot process the
"Issue Refund" action unless the customer meets the criteria?

正解：A

解説：
The correct answer is C because this requirement must be enforced deterministically at the action- availability layer, not trusted to natural language instructions. In Agentforce, filters can control access to subagents and actions so the agent can use them only when defined conditions are met. For refund processing, the "Issue Refund" action should be hidden from the reasoning engine unless the account tier condition evaluates to Platinum. Option A is weak because system instructions are interpreted by the model and can be ignored or misapplied. Option B is wrong because blocking a keyword is brittle and does not validate entitlement. Action filters are the correct control because they prevent the action from being available until the business rule is satisfied.


質問 # 357
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当社のAgentforce-Specialist試験資料は、この時代の製品であり、時代全体の開発動向に適合しています。覚えているので、私たちは勉強と試験の状態にあり、無数のテストを経験しているようです。就職活動の過程で、私たちは常に何が達成され、どのような証明書を取得したのかと尋ねられます。したがって、私たちはテストAgentforce-Specialist認定を取得し、資格認定を取得して定量的標準になります。また、当社のAgentforce-Specialist学習ガイドは、ごく短時間で最速を証明するのに役立ちます。

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