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究極のガイドはAgentforce-Specialist日本語最新2025年12月13日時間限定！今すぐダウンロード！ [Q39-Q63]

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究極のガイドはAgentforce-Specialist日本語最新2025年12月13日時間限定！今すぐダウンロード！

2025年最新のな厳密検証された合格させるAgentforce-Specialist日本語試験にはリアル問題と解答

質問 # 39
Universal Containers (UC) は、Generative AI Salesforce 機能を使用して、既存のナレッジ記事に基づいて推奨される返信を提供することで、サービス エージェントの処理時間を短縮したいと考えています。UC はどの AI 機能についてサービス エージェントをトレーニングする必要がありますか?

  • A. 知識の返答
  • B. サービス応答
  • C. ケース返信

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation:Salesforce Agentforce leverages generative AI to enhance service agent efficiency, particularly through capabilities that generate recommended replies. In this scenario, Universal Containers aims to reduce handling time by providing replies based on existing Knowledge articles
, which are a core component of Salesforce Knowledge. The Knowledge Replies capability is specifically designed for this purpose-it uses generative AI to analyze Knowledge articles, match them to the context of a customer inquiry (e.g., a case or chat), and suggest relevant, pre-formulated responses for service agents to use or adapt. This aligns directly with UC's goal of leveraging existing content to streamline agent workflows.
* Option A (Service Replies): While "Service Replies" might sound plausible, it is not a specific, documented capability in Agentforce. It appears to be a generic distractor and does not tie directly to Knowledge articles.
* Option B (Case Replies): "Case Replies" is not a recognized AI capability in Agentforce either. While replies can be generated for cases, the focus here is on Knowledge article integration, which points to Knowledge Replies.
* Option C (Knowledge Replies): This is the correct capability, as it explicitly connects generative AI with Knowledge articles to produce recommended replies, reducing agent effort and handling time.
Training service agents on Knowledge Replies ensures they can effectively use AI-suggested responses, review them for accuracy, and integrate them into their workflows, fulfilling UC's objective.
References:
* Salesforce Agentforce Documentation: "Knowledge Replies for Service Agents" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5)
* Trailhead: "Agentforce for Service" module (https://trailhead.salesforce.com/content/learn/modules
/agentforce-for-service)


質問 # 40
カスタマー サービス担当者は、旅行情報を保存するカスタム オブジェクトを確認しています。最近、気象警報を受け取ったため、この旅程に関連する顧客のフライトをキャンセルする必要があります。担当者は、顧客のフライトのキャンセルと再予約に関するナレッジ記事を確認する必要があります。
担当者がこれを達成するのに役立つ Agentforce の機能はどれですか?

  • A. エージェントがフライトをキャンセルする手順を作成するために入力したプロンプトに基づいてナレッジ記事を生成します。
  • B. 外部データを呼び出してナレッジ記事を作成するフローを呼び出します。
  • C. 利用可能なアクションに基づいてタスクを実行し、アクセス可能なナレッジ記事の情報を使用して質問に答えます。

正解：C

解説：
The scenario involves a customer service representative needing to cancel flights due to a weather alert and review existing Knowledge articles for guidance on canceling and rebooking. Agentforce provides capabilities to streamline such tasks. The most suitable option is Option B, which allows the agent to
"execute tasks based on available actions" (e.g., canceling flights via a predefined action) while "answering questions using information from accessible Knowledge articles." This capability leverages Agentforce's ability to integrate Knowledge articles into the agent's responses, enabling the representative to ask questions (e.g., "How do I cancel a flight?") and receive AI-generated answers grounded in approved Knowledge content. Simultaneously, the agent can trigger actions (e.g., a Flow to update the custom object) to perform the cancellations, meeting all requirements efficiently.
* Option A: Invoking a Flow to call external data and create a Knowledge article is unnecessary. The representative needs to review existing articles, not create new ones, and there's no indication external data is required for this task.
* Option B: This is correct. It combines task execution (canceling flights) with Knowledge article retrieval, aligning with the representative's need to act and seek guidance from existing content.
* Option C: Generating a new Knowledge article based on prompts is not relevant. The representative needs to use existing articles, not author new ones, especially in a time-sensitive weather alert scenario.
Option B best supports the representative's workflow in Agentforce.
:
Salesforce Agentforce Documentation: "Knowledge Replies and Actions" (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.agentforce_knowledge_replies.htm&type=5) Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce-for- service)


質問 # 41
Universal Containers (UC) は、エージェントを本番環境に導入する前に、その有効性、信頼性、信頼性を確認したいと考えています。UC は、大量の繰り返し可能な発話を効率的にテストしたいと考えています。
Agentforce スペシャリストは何を推奨すべきでしょうか?

  • A. エージェントを QA サンドボックス環境にデプロイし、発話分析レポートを確認して有効性を確認します。
  • B. テスト テンプレートを使用して、Agentforce テスト センターで UC のテスト ケースを含む CSV ファイルを作成します。
  • C. エージェントの大規模言語モデル (LLM) UI を活用し、エージェントをアクティブ化する前に、さまざまな発話で UC のエージェントをテストします。

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:
The goal of Universal Containers (UC) is to test its Agentforce agents for effectiveness, reliability, and trust before production deployment, with a focus on efficiently handling alarge and repeatable number of utterances. Let's evaluate each option against this requirement and Salesforce's official Agentforce tools and best practices.
* Option A: Leverage the Agent Large Language Model (LLM) UI and test UC's agents with different utterances prior to activating the agent.While Agentforce leverages advanced reasoning capabilities (powered by the Atlas Reasoning Engine), there's no specific "Agent Large Language Model (LLM) UI" referenced in Salesforce documentation for testing agents. Testing utterances directly within an LLM interface might imply manual experimentation, but this approach lacks scalability and repeatability for a large number of utterances. It's better suited for ad-hoc testing of individual responses rather than systematic evaluation, making it inefficient for UC's needs.
* Option B: Deploy the agent in a QA sandbox environment and review the Utterance Analysis reports to review effectiveness.Deploying an agent in a QA sandbox is a valid step in the development lifecycle, as sandboxes allow testing in a production-like environment without affecting live data.
However, "Utterance Analysis reports" is not a standard term in Agentforce documentation. Salesforce provides tools like Agent Analytics or User Utterances dashboards for post-deployment analysis, but these are more about monitoring live performance than pre-deployment testing. This option doesn't explicitly address how to efficiently test alarge and repeatable number of utterancesbefore deployment, making it less precise for UC's requirement.
* Option C: Create a CSV file with UC's test cases in Agentforce Testing Center using the testing template.The Agentforce Testing Center is a dedicated tool within Agentforce Studio designed specifically for testing autonomous AI agents. According to Salesforce documentation, Testing Center allows users to upload a CSV file containing test cases (e.g., utterances and expected outcomes) using a provided template. This enables the generation and execution of hundreds of synthetic interactions in parallel, simulating real-world scenarios. The tool evaluates how the agent interprets utterances, selects topics, and executes actions, providing detailed results for iteration. This aligns perfectly with UC's need for efficiency (bulk testing via CSV), repeatability (standardized test cases), and reliability (systematic validation), ensuring the agent is production-ready. This is the recommended approach per official guidelines.
Why Option C is Correct:
The Agentforce Testing Center is explicitly built for pre-deployment validation of agents. It supports bulk testing by allowing users to upload a CSV with utterances, which is then processed by the Atlas Reasoning Engine to assess accuracy and reliability. This method ensures UC can systematically test a large dataset, refine agent instructions or topics based on results, and build trust in the agent's performance-all before production deployment. This aligns with Salesforce's emphasis on testing non-deterministic AI systems efficiently, as noted in Agentforce setup documentation and Trailhead modules.
References:
Salesforce Trailhead: Get Started with Salesforce Agentforce Specialist Certification Prep- Details the use of Agentforce Testing Center for testing agents with synthetic interactions.
Salesforce Agentforce Documentation: Agentforce Studio > Testing Center- Explains how to upload CSV files with test cases for parallel testing.
Salesforce Help: Agentforce Setup > Testing Autonomous AI Agents- Recommends Testing Center for pre- deployment validation of agent effectiveness and reliability.


質問 # 42
オプションを 1 つ選択します。
Universal Containers の管理者は、サンドボックス環境で新しいエージェントを開発し、それを本番環境に展開したいと考えています。
エージェントを展開するには管理者は何をすべきでしょうか?

  • A. 必要なすべてのエージェント コンポーネントを含む送信変更セットを作成し、本番環境にアップロードします。
  • B. エージェント コンポーネントを JSON ファイルとしてエクスポートし、メタデータ API を使用して手動で本番環境にインポートします。
  • C. 変更セットが使用できないため、エージェント構成、トピック、およびアクションを手動で本番環境で再作成します。

正解：A

解説：
As per the AgentForce Deployment and Lifecycle Management Guide, AgentForce agents, including their topics, actions, and prompt templates, can be deployed from sandbox to production using Salesforce change sets.
Administrators should create an outbound change set in the sandbox environment that includes all relevant components (Agent definition, prompt templates, topic configurations, flows, and permissions) and then upload it to production for validation and deployment. This process ensures consistency, auditability, and proper dependency tracking between environments.
Option A is incorrect because manual recreation is inefficient and error-prone. Option B is not recommended, as JSON export/import is for development backups and advanced metadata operations, not for standard deployments.
Hence, the correct approach per AgentForce's best practices is Option C - Create an outbound change set and deploy it to production.
Reference: AgentForce Administration Guide - "Deploying Agents and Components via Change Sets."


質問 # 43
管理者はFlexプロンプトの応答を確認したい
テンプレートを作成しましたが、プレビュー ボタンがグレー表示されています。
その理由は何でしょうか?

  • A. プロンプトにマージ フィールドが挿入されていません。
  • B. プロンプトは保存されておらず、アクティブ化されていません。
  • C. プロンプトに関連するレコードが選択されていません。

正解：C

解説：
When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.
* Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.
* Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.
Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.
Salesforce Agentforce Specialist References:Refer to the documentation on troubleshooting Flex templates here: https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm


質問 # 44
Agentforceスペシャリストが、フロー上に構築されたカスタムアクションを使用する新しいサービスエージェントを構築します。エージェントはサンドボックスでテストされ、デプロイの準備が整いました。
実稼働環境でのエージェントのアクティベーション ステータスに関する重要な考慮事項は何ですか?

  • A. フローもアクティブな場合にのみ、エージェントが自動的にアクティブ化されます。
  • B. エージェントは、展開が成功すると自動的にアクティブ化されます。
  • C. エージェントは、サンドボックス内のステータスに関係なく、本番環境で手動でアクティブ化する必要があります。

正解：C

解説：
According to the AgentForce Deployment and Lifecycle Management Guide, when an agent is deployed from a sandbox to a production environment, activation does not carry over automatically. The documentation clarifies: "Each environment maintains its own activation state. Agents must be manually activated in production after deployment to ensure controlled rollout and compliance validation." This ensures that only verified configurations are activated intentionally. Option A is incorrect because activation is not dependent solely on the flow's active status. Option C is also incorrect, as automatic activation upon deployment is explicitly prevented by design to maintain environment safety.
Therefore, Option B correctly reflects the deployment requirement for manual activation in production.
References (AgentForce Documents / Study Guide):
AgentForce Deployment Guide: "Activating Agents in Production Environments" AgentForce Implementation Handbook: "Environment Lifecycle and Activation Controls" Salesforce Release Management Study Notes: "Post-Deployment Activation Steps"


質問 # 45
Universal Containers は、Agentforce for Sales を使用して類似の商談を見つけ、取引をより迅速に成立させようとしています。チームは、エージェントが商談をマッチングするために使用する基準を理解したいと考えています。Agentforce for Sales が類似の商談をマッチングするために使用する 1 つの基準は何ですか?

  • A. 一致する商談は過去 12 か月以内に作成されました。
  • B. マッチングされた商談は同じアカウントに限定されます。
  • C. 一致した商談のステータスは、過去 12 か月間で「受注成立」です。

正解：C

解説：
UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure. Let's determine a criterion used by the "Find Similar Opportunities" feature.
* Option A: Matched opportunities have a status of Closed Won from the last 12 months.Agentforce for Sales analyzes historical data to find similar opportunities, prioritizing "Closed Won" deals as successful examples. Documentation specifies a 12-month lookback period for relevance, ensuring recent, applicable matches. This is a key criterion, making it the correct answer.
* Option B: Matched opportunities are limited to the same account.While account context may factor in, Agentforce doesn't restrict matches to the same account-it considers broader patterns across opportunities (e.g., industry, deal size). This is too narrow and incorrect.
* Option C: Matched opportunities were created in the last 12 months.Creation date isn't a primary criterion-status (e.g., Closed Won) and recency of closure matter more. This doesn't align with documented behavior, making it incorrect.
Why Option A is Correct:
"Closed Won" status within 12 months is a documented criterion for Agentforce's similarity matching, providing actionable insights for deal closure.
References:
Salesforce Agentforce Documentation: Agentforce for Sales > Find Similar Opportunities - Specifies Closed Won, 12-month criterion.
Trailhead: Explore Agentforce Sales Agents - Details opportunity matching logic.
Salesforce Help: Sales Features in Agentforce - Confirms historical success focus.


質問 # 46
Universal Containers は、CRM データを、大規模言語モデル (LLM) へのプロンプトで適切にフォーマットされた JSON として組み込むことを希望しています。
この要件に関して重要な考慮事項は何ですか?

  • A. Apex コードを使用して、JSON 形式の差し込み項目を返すことができます。
  • B. プロンプト テンプレートを作成するときは、「CRM データを JSON に変換」チェックボックスをオンにする必要があります。
  • C. プロンプトビルダー設定で JSON 形式を有効にする必要があります。

正解：A

解説：
* Context of the Question
* Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language model (LLM).
* The question is about an important technical or design consideration for including CRM data as JSON in that prompt.
* Why Apex Code for JSON Formatting?
* Apex to Generate JSON: Salesforce does not have a simple "checkbox" or single setting to
"convert CRM data to JSON." Typically, to structure data as JSON in a template, you either:
* Use an Apex class that queries or processes the data, then returns a JSON string.
* Use a Flow or formula approach (though complex data structures often require Apex).
* No Built-In "Enable JSON Format in Prompt Builder": Prompt Builder doesn't have a toggle that automatically transforms data into JSON.
* ConclusionThe practical solution to pass CRM data in JSON format to an LLM is to use Apex code (or a specialized Flow approach) to produce a JSON string, which the prompt can then merge and pass along. Hence, Option B is correct.
Salesforce Agentforce Specialist References & Documents
* Salesforce Documentation: Working with JSON in ApexDescribes how to serialize and deserialize data using Apex for integration or AI prompts.
* Salesforce Agentforce Specialist Study GuideEmphasizes the need for custom logic (often in Apex) when complex data transformations (like JSON formatting) are required.


質問 # 47
Universal Containers では、個々の顧客保証を外部システムから Data Cloud に取り込む必要があります。
保証ステータスがアクティブなアカウントに対してのみ、Agentforce が保証関連の応答を返すようにしたいと考えています。
保証関連情報が正しく取得されるようにするには、Agentforce スペシャリストはどの検索アプローチを設定する必要がありますか?

  • A. Agentforce の指示に従って保証制約を適用し、WarrantyStatus = Active の結果のみを含めます。
  • B. ハイブリッド検索を使用し、一致するアカウントとWarrantyStatus = Activeフィールドに対して新しいカスタムリトリーバーで事前フィルタリングを適用します。
  • C. アカウントの保証ステータスを Agentforce カスタム変数に保存し、取得時に保証を動的にフィルタリングします。

正解：B

解説：
The AgentForce Retrieval Configuration Guide outlines that when external data must be filtered by specific criteria-such as WarrantyStatus = Active-the correct approach is to use Hybrid Search with pre-filtering logic defined in a custom retriever. The documentation specifies: "Hybrid search allows combining keyword precision and semantic context. When used with a custom retriever, administrators can pre-filter data on metadata fields (e.g., WarrantyStatus) to ensure that only eligible records are returned." Option A (relying on instructions) introduces non-deterministic behavior because the LLM cannot enforce data constraints. Option B (custom variable filtering) applies post-retrieval filtering, which is less efficient and less secure than index-level filtering. Therefore, Option C aligns with Salesforce's best practice for deterministic, metadata-based retrieval control.
References (AgentForce Documents / Study Guide):
* AgentForce Data Cloud Retrieval Guide: "Building Custom Retrievers with Metadata Filters"
* Einstein Studio for AgentForce: "Hybrid Search Pre-Filtering"
* AgentForce Study Guide: "Filtering Warranty Data by Active Status"


質問 # 48
Universal Containers は、AI を使用して営業チームの生産性を向上させる予定です。Prompt Builder の使用が必要な具体的な要件は何ですか?

  • A. 今後開催される展示会のニュースレターの下書きを作成しています。
  • B. 過去の購入データを使用して推定顧客生涯価値 (CLV) を作成します。
  • C. 顧客が会社との関係を断つ可能性を予測します。

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation:
UC seeks an AI solution for sales productivity. Let's determine which requirement aligns with Prompt Builder.
* Option A: Creating a draft newsletter for an upcoming tradeshow.Prompt Builder excels at generating text outputs (e.g., newsletters) using Generative AI. UC can create a prompt template to draft personalized, context-rich newsletters based on sales data, boosting productivity. This matches Prompt Builder's capabilities, making it the correct answer.
* Option B: Predicting the likelihood of customers churning or discontinuing their relationship with the company.Churn prediction is a predictive AI task, suited for Einstein Prediction Builder or Data Cloud models, not Prompt Builder, which focuses on generative tasks. This is incorrect.
* Option C: Creating an estimated Customer Lifetime Value (CLV) with historical purchase data.
CLV estimation involves predictive analytics, not text generation, and is better handled by Einstein Analytics or custom models, not Prompt Builder. This is incorrect.
Why Option A is Correct:
Drafting newsletters is a generative task uniquely suited to Prompt Builder, enhancing sales productivity as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Prompt Builder > Use Cases- Lists text generation like newsletters.
Trailhead: Build Prompt Templates in Agentforce- Covers productivity-enhancing text outputs.
Salesforce Help: Generative AI with Prompt Builder- Confirms drafting capabilities.


質問 # 49
Agentforce は、フローを参照アクション タイプとして使用して、コパイロット カスタム アクションを作成しました。ただし、会話プレビューに期待どおりの結果が提供されないため、トラブルシューティングが必要です。
問題の根本原因を特定するために Agentforce スペシャリストは何をすべきでしょうか?

  • A. ダイナミック パネル内の Copilot Builder で、選択したアクションを確認し、入力セクションと出力セクションの値を確認します。
  • B. Copilot Builder で、ユーザーが入力した発話を検証し、デバッグ情報についてセッション イベント ログを確認します。
  • C. Copilot Builder の Dynamic Panel 内で、動的デバッグをオンにして入力と出力を表示します。

正解：C

解説：
When troubleshooting acopilot custom actionusing flow as the reference action type, enablingdynamic debuggingwithinCopilot Builder's Dynamic Panelis the most effective way to identify the root cause. By turning on dynamic debugging, the Agentforce Specialist can see detailed logs showing both theinputs and outputsof the flow, which helps identify where the action might be failing or not delivering the expected results.
* Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with dynamic debugging.
* Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.
Salesforce Agentforce Specialist References:
To explore more about dynamic debugging in Copilot Builder, see:https://help.salesforce.com/s/articleView?
id=sf.copilot_custom_action_debugging.htm


質問 # 50
Agentforce によってカスタム エージェント アクションが作成されましたが、プランナー サービスによって正しい順序で取得されません。
プランナー サービスが期待どおりに動作するために、AI スペシャリストはカスタム エージェント アクション指示でどのような調整を行う必要がありますか?

  • A. アクションを呼び出すために使用する LLM モデル プロバイダーとバージョンを指定します。
  • B. アクション API 名を参照して依存アクションを指定します。
  • C. アクションを呼び出すことが許可されるプロファイルまたはカスタム権限を指定します。

正解：B

解説：
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist must explicitly specify dependent actions using their API names in the custom action's configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A's API name must be listed as a dependency in Action B's instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.
In contrast:
* Profiles or custom permissions (B) control access to the action but do not influence execution order.
* LLM model provider and version (C) determine the AI model used for processing but are unrelated to the planner's sequencing logic.
Reference:
Salesforce Help Article: Configure Custom Actions for Einstein Bots (Section: "Defining Action Dependencies").
Einstein Bot Developer Guide: "Orchestrating Workflows with the Planner Service" (Dependency Management best practices).


質問 # 51
意図を理解し、エージェントアクションを実行する上で、大規模言語モデル (LLM) の役割は何ですか?

  • A. ユーザーのトピック アクセスを決定し、実行するアクションを優先度順に並べ替えます。
  • B. 要求された同様のトピックを検索し、実行する必要があるアクションを提供します。
  • C. 最も一致するトピックとアクション、および正しい実行順序を特定します。

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions. Let's evaluate its role.
* Option A: Find similar requested topics and provide the actions that need to be executed.While the LLM can identify similar topics, its role extends beyond merely finding them-it matches intents to specific topics and determines execution. This option understates the LLM's responsibility for ordering actions, making it incomplete and incorrect.
* Option B: Identify the best matching topic and actions and correct order of execution.The LLM analyzes user input to understand intent, matches it to the best-fitting topic (configured in Agent Builder), and selects associated actions. It also determines the correct sequence of execution based on the agent's plan (e.g., retrieve data before updating a record). This end-to-end process-from intent recognition to action orchestration-is the LLM's core role in Agentforce, making this the correct answer.
* Option C: Determine a user's topic access and sort actions by priority to be executed.Topic access is governed by Salesforce permissions (e.g., user profiles), not the LLM. While the LLM prioritizes actions within its plan, its primary role is intent matching and execution ordering, not access control, making this incorrect.
Why Option B is Correct:
The LLM's role in identifying topics, selecting actions, and ordering execution is central to Agentforce's autonomous functionality, as detailed in Salesforce documentation.
References:
Salesforce Agentforce Documentation: Atlas Reasoning Engine- Outlines LLM's intent and action handling.
Trailhead: Understand Agentforce Technology- Explains topic matching and execution.
Salesforce Help: Agentforce Actions- Confirms LLM's role in orchestrating responses.


質問 # 52
Universal Containers のエージェント アクションには、新しい Agentforce エージェント用の Apex クラスがいくつか含まれています。エージェント アクションによって呼び出される Apex を展開する場合の重要な考慮事項は何ですか?

  • A. Apex クラスは、単体テストで少なくとも 75% のコード カバレッジを持ち、すべての依存関係がデプロイメント パッケージに含まれている必要があります。
  • B. エージェントアクションによって呼び出される Apex クラスは、エージェントが本番環境でアクティブ化されていない限り、75% 未満のテスト カバレッジでデプロイできます。
  • C. Apex クラスは、エージェントによってのみ使用される限り、75% のコード カバレッジ要件をバイパスできます。

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation:
Universal Containers (UC) is using Apex classes within an Agent Action for their Agentforce Agent.
Deploying Apex in Salesforce has specific requirements, especially when tied to Agentforce functionality. Let' s evaluate the options.
* Option A: The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.Salesforce enforces a strict requirement that all Apex classes must achieve at least 75% code coverage from unit tests for deployment to production, regardless of their use case (e.g., Agentforce, triggers, or web services). Additionally, when Apex is invoked by an Agent Action (e.g., via a Flow or direct invocation), all dependencies (e.g., referenced classes, objects) must be included in the deployment package to ensure functionality. This is a standard deployment consideration in Salesforce and applies to Agentforce, making this the correct answer.
* Option B: Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.Salesforce's 75% code coverage requirement is mandatory for production deployment, regardless of whether the agent is activated.
There's no exemption based on activation status-coverage is enforced at the deployment stage. This option is incorrect and contradicts Salesforce's Apex deployment rules.
* Option C: The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.No such bypass exists in Salesforce. The 75% code coverage rule applies universally to all Apex in production, including classes used by Agentforce. Agent-specific usage doesn' t waive this requirement, making this incorrect.
Why Option A is Correct:
The 75% code coverage requirement and inclusion of dependencies are fundamental Salesforce deployment rules, applicable to Apex in Agent Actions. This ensures reliability and functionality in production, as per official documentation.
References:
Salesforce Agentforce Documentation: Agent Builder > Custom Actions > Apex- Notes standard Apex deployment rules apply.
Salesforce Developer Guide: Apex Testing- Confirms 75% coverage requirement.
Trailhead: Deploy Apex Code- Emphasizes coverage and dependencies for production.


質問 # 53
Universal Containers は、Einstein Trust Layer を Einstein Generative AI Audit Data と組み合わせて活用することを検討しています。
Einstein Trust Layer を使用して利用できる監査データはどれですか?

  • A. 幻覚スコアとバイアススコア
  • B. マスクされたデータと毒性スコア
  • C. 応答精度と攻撃性スコア

正解：B

解説：
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information about masked data-which ensures sensitive information is not exposed-and the toxicity score, which evaluates the generated content for inappropriate or harmful language.
References:
* Salesforce Agentforce Specialist Documentation - Einstein Trust Layer: Details the auditing capabilities, including logging of masked data and evaluation of generated responses for toxicity to maintain compliance and trust.


質問 # 54
Universal Containers (UC) は、営業チームが AI を使用してカタログから推奨製品を提案できるようにしたいと考えています。UC はどのタイプのプロンプト テンプレートを使用すればよいでしょうか?

  • A. レコード要約プロンプトテンプレート
  • B. Flex プロンプト テンプレート
  • C. メール生成プロンプトテンプレート

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:
UC needs an AI solution to suggest products from a catalog for its sales team. Let's assess the prompt template types in Prompt Builder.
* Option A: Record summary prompt templateRecord summary templates generate concise summaries of records (e.g., Case, Opportunity). They're not designed for product recommendations, which require dynamic logic beyond summarization, making this incorrect.
* Option B: Email generation prompt templateEmail generation templates craft emails (e.g., customer outreach). While they could mention products, they're not optimized for standalone recommendations, making this incorrect.
* Option C: Flex prompt templateFlex prompt templates are versatile, allowing custom inputs (e.g., catalog data from objects or Data Cloud) and instructions (e.g., "Suggest products based on customer preferences"). This flexibility suits UC's need to recommend products dynamically, making it the correct answer.
Why Option C is Correct:
Flex templates offer the customization needed to suggest products from a catalog, aligning with Salesforce's guidance for tailored AI outputs.
References:
Salesforce Agentforce Documentation: Prompt Builder > Flex Templates- Details dynamic use cases.
Trailhead: Build Prompt Templates in Agentforce- Covers Flex for custom scenarios.
Salesforce Help: Prompt Template Types- Confirms Flex versatility.


質問 # 55
アップロードされたファイルを含む Agentforce データ ライブラリの場合、作成および構成されると何が起こりますか?

  • A. アップロードされたファイルをユーザーが指定した場所にインデックスします。
  • B. Salesforceファイルストレージにアップロードされたファイルをインデックスします。
  • C. アップロードされたファイルをデータクラウドにインデックスします

正解：C

解説：
In Salesforce Agentforce, a Data Library is a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs.
Salesforce Agentforce integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud's vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.
* Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
* Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector database to support AI capabilities like semantic search and content retrieval.
* Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.
:
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: "Vector Database for AI" (https://help.salesforce.com/s/articleView?
id=sf.data_cloud_vector_database.htm&type=5)


質問 # 56
Universal Containers (UC) は、標準基盤モデルを活用するプロンプト テンプレートを実装する予定です。
プロンプトビルダーでプロンプトテンプレートを作成するときに、UC が考慮すべきことは何ですか?

  • A. LLM のコンテキスト理解をテストするために、プロンプト内に複数選択の質問を含めます。
  • B. 単語の選択、強調語、絵文字、句読点などのさまざまな書き方を使用したデータで LLM をトレーニングします。
  • C. LLM にさらにコンテキストを提供するために、プロンプト テンプレート内のキャラクターとしてロールプレイするように依頼します。

正解：C

解説：
UC is using Prompt Builder with standard foundation models (e.g., via Atlas Reasoning Engine). Let's assess best practices for prompt design.
* Option A: Include multiple-choice questions within the prompt to test the LLM's understanding of the context.Prompt templates are designed to generate responses, not to test the LLM with multiple- choice questions. This approach is impractical and not supported by Prompt Builder's purpose, making it incorrect.
* Option B: Ask it to role-play as a character in the prompt template to provide more context to the LLM.A key consideration in Prompt Builder is crafting clear, context-rich prompts. Instructing the LLM to adopt a role (e.g., "Act as a sales expert") enhances context and tailors responses to UC's needs, especially with standard models. This is a documented best practice for improving output relevance, making it the correct answer.
* Option C: Train LLM with data using different writing styles including word choice, intensifiers, emojis, and punctuation.Standard foundation models in Agentforce are pretrained and not user- trainable. Prompt Builder users refine prompts, not the LLM itself, making this incorrect.
Why Option B is Correct:
Role-playing enhances context for standard models, a recommended technique in Prompt Builder for effective outputs, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: Prompt Builder > Best Practices - Recommends role-based context.
Trailhead: Build Prompt Templates in Agentforce - Highlights role-playing for clarity.
Salesforce Help: Prompt Design Tips - Suggests contextual roles.


質問 # 57
Universal Containers では、営業担当者がプロンプト テンプレートのみを実行できるようにする必要があります。この要件を満たすには、会社は何を使用すればよいでしょうか。

  • A. テンプレートの実行を促す権限セット
  • B. プロンプトテンプレートユーザー権限設定
  • C. プロンプトテンプレートマネージャの権限設定

正解：B

解説：
Salesforce Agentforce leverages Prompt Builder, a powerful tool that allows administrators to create and manage prompt templates, which are reusable frameworks for generating AI-driven responses. These templates can be invoked by users to perform specific tasks, such as generating sales emails or summarizing records, based on predefined instructions and grounded data. In this scenario, Universal Containers wants its sales reps to have the ability to only execute these prompt templates, meaning they should be able to run them but not create, edit, or manage them.
Let's break down the options and analyze why B. Prompt Template User permission set is the correct answer:
Option A: Prompt Execute Template permission setThis option sounds plausible at first glance because it includes the phrase "Execute Template," which aligns with the requirement. However, there is no specific permission set named "Prompt Execute Template" in Salesforce's official documentation for Prompt Builder or Agentforce. Salesforce typically uses more standardized naming conventions for permission sets, and this appears to be a distractor option that doesn't correspond to an actual feature. Permissions in Salesforce are granular, but they are grouped logically under broader permission sets rather than hyper-specific ones like this.
Option B: Prompt Template User permission setThis is the correct answer. In Salesforce, the Prompt Builder feature, which is integral to Agentforce, includes permission sets designed to control access to prompt templates. The "Prompt Template User" permission set is an official Salesforce permission set that grants users the ability to execute (or invoke) prompt templates without giving them the ability to create or modify them. This aligns perfectly with the requirement that sales reps should only execute prompt templates, not manage them. The Prompt Template User permission set typically includes permissions like "Run Prompt Templates," which allows users to trigger templates from interfaces such as Lightning record pages or flows, while restricting access to the Prompt Builder setup area where templates are designed.
Option C: Prompt Template Manager permission setThis option is incorrect because the "Prompt Template Manager" permission set is designed for users who need full administrative control over prompt templates.
This includes creating, editing, and deleting templates in Prompt Builder, in addition to executing them. Since Universal Containers only wants sales reps to execute templates and not manage them, this permission set provides more access than required, violating the principle of least privilege-a key security best practice in Salesforce.
How It Works in Salesforce
To implement this, an administrator would:
Navigate to Setup > Permission Sets.
Locate or create the "Prompt Template User" permission set (this is a standard permission set available with Prompt Builder-enabled orgs).
Assign this permission set to the sales reps' profiles or individual user records.
Ensure the prompt templates are configured and exposed (e.g., via Lightning components like the Einstein Summary component) on relevant pages, such as Opportunity or Account record pages, where sales reps can invoke them.
Why This Matters
By assigning the Prompt Template User permission set, Universal Containers ensures that sales reps can leverage AI-driven prompt templates to enhance productivity (e.g., drafting personalized emails or generating sales pitches) while maintaining governance over who can modify the templates. This separation of duties is critical in a secure Salesforce environment.
References to Official Salesforce Agentforce Specialist Documents
Salesforce Help: Prompt Builder PermissionsThe official Salesforce documentation outlines permission sets for Prompt Builder, including "Prompt Template User" for execution-only access and "Prompt Template Manager" for full control.
Trailhead: Configure Agentforce for ServiceThis module discusses how permissions are assigned to control Agentforce features, including prompt-related capabilities.
Salesforce Ben: Why Prompt Builder Is Vital in an Agentforce World (November 25, 2024)This resource explains how Prompt Builder integrates with Agentforce and highlights the use of permission sets like Prompt Template User to enable end-user functionality.


質問 # 58
Universal Containers は、顧客サービス業務を強化するためにカスタム エージェント アクションを実装しています。開発チームは、適切な構成と機能を確保するために、カスタム エージェント アクションのコア コンポーネントを理解する必要があります。カスタム エージェント アクションのコア コンポーネントの 1 つを識別するために、開発チームはカスタム エージェント アクション構成で何を確認する必要がありますか?

  • A. 出力タイプ
  • B. アクショントリガー
  • C. 指示

正解：C

解説：
UC's development team needs to identify a core component of a Custom Agent Action in Agent Builder. Let' s assess the options.
Option A: Action Triggers"Action Triggers" isn't a term used in Agentforce Custom Agent Action configuration. Actions are invoked by topics or plans, not standalone triggers, making this incorrect.
Option B: InstructionsInstructions are a core component of a Custom Agent Action in Agentforce. Defined in Agent Builder, they guide the Atlas Reasoning Engine on how to execute the action (e.g., what to do with inputs, how to process data). Reviewing the instructions helps the team understand the action's purpose and logic, making this the correct answer.
Option C: Output TypesWhile outputs are part of an action's result, "Output Types" isn't a distinct configuration element in Agent Builder. Outputs are determined by the action's execution (e.g., Flow or Apex), not a separate setting, making this less core and incorrect.
Why Option B is Correct:
Instructions are a fundamental component of Custom Agent Actions, providing the AI's execution directives, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Agent Builder > Custom Actions - Highlights instructions as key.
Trailhead: Build Agents with Agentforce - Details configuring actions with instructions.
Salesforce Help: Create Custom Actions - Confirms instructions' role.


質問 # 59
Universal Containers (UC) は、標準基盤モデルを活用するプロンプト テンプレートを実装する予定です。
プロンプトビルダーでプロンプトテンプレートを作成するときに、UC が考慮すべきことは何ですか?

  • A. LLM のコンテキスト理解をテストするために、プロンプト内に複数選択の質問を含めます。
  • B. 単語の選択、強調語、絵文字、句読点などのさまざまな書き方を使用したデータで LLM をトレーニングします。
  • C. LLM にさらにコンテキストを提供するために、プロンプト テンプレート内のキャラクターとしてロールプレイするように依頼します。

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
UC is using Prompt Builder with standard foundation models (e.g., via Atlas Reasoning Engine). Let's assess best practices for prompt design.
* Option A: Include multiple-choice questions within the prompt to test the LLM's understanding of the context.Prompt templates are designed to generate responses, not to test the LLM with multiple- choice questions. This approach is impractical and not supported by Prompt Builder's purpose, making it incorrect.
* Option B: Ask it to role-play as a character in the prompt template to provide more context to the LLM.A key consideration in Prompt Builder is crafting clear, context-rich prompts. Instructing the LLM to adopt a role (e.g., "Act as a sales expert") enhances context and tailors responses to UC's needs, especially with standard models. This is a documented best practice for improving output relevance, making it the correct answer.
* Option C: Train LLM with data using different writing styles including word choice, intensifiers, emojis, and punctuation.Standard foundation models in Agentforce are pretrained and not user- trainable. Prompt Builder users refine prompts, not the LLM itself, making this incorrect.
Why Option B is Correct:
Role-playing enhances context for standard models, a recommended technique in Prompt Builder for effective outputs, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: Prompt Builder > Best Practices- Recommends role-based context.
Trailhead: Build Prompt Templates in Agentforce- Highlights role-playing for clarity.
Salesforce Help: Prompt Design Tips- Suggests contextual roles.


質問 # 60
データ サイエンス チームは、Databricks で製品推奨用の XGBoost 分類モデルをトレーニングしました。Agentforce スペシャリストは、このモデルから製品推奨の推論をスタンドアロン データ モデル オブジェクト (DMO) として Data Cloud に取り込むことを任されています。
Agentforce スペシャリストはこれをどのように設定すればよいでしょうか?

  • A. Databricks でサービス エンドポイントを作成し、モデル ビルダーを使用してモデルを構成します。
  • B. Einstein Studio でサービスエンドポイントを作成し、モデルビルダーを使用してモデルを設定します。
  • C. Databricks でサービス エンドポイントを作成し、Python SDK コネクタを使用してモデルを構成します。

正解：A

解説：
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
Reference:
"Einstein Studio and Model Integration with External Endpoints | Salesforce Trailhead" .


質問 # 61
Universal Containers は、Agentforce for Sales を利用して、取引成立のためのガイダンスと手順を含む AI 生成プランを提供することで、営業担当者が販売ノルマを達成できるようにしたいと考えています。この要件を満たす機能はどれですか。

  • A. 類似の取引を探す
  • B. クローズプランの作成
  • C. アカウントプランの作成

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) aims to leverage Agentforce for Sales to assist sales reps with AI-generated plans that provide guidance and steps for closing deals. Let's evaluate the options based on Agentforce for Sales features.
* Option A: Create Account PlanWhile account planning is valuable for long-term strategy, Agentforce for Sales does not have a specific "Create Account Plan" feature focused on closing individual deals.
Account plans typically involve broader account-level insights, not deal-specific closure steps, making this incorrect for UC's requirement.
* Option B: Find Similar Deals"Find Similar Deals" is not a documented feature in Agentforce for Sales. It might imply identifying past deals for reference, but it doesn't involve generating plans with guidance and steps for closing current deals. This option is incorrect and not aligned with UC's goal.
* Option C: Create Close PlanThe "Create Close Plan" feature in Agentforce for Sales uses AI to generate a detailed plan with actionable steps and guidance tailored to closing a specific deal. Powered by the Atlas Reasoning Engine, it analyzes deal data (e.g., Opportunity records) and provides reps with a roadmap to meet quotas. This directly meets UC's requirement for AI-generated plans focused on deal closure, making it the correct answer.
Why Option C is Correct:"Create Close Plan" is a specific Agentforce for Sales capability designed to help reps close deals with AI-driven plans, aligning perfectly with UC's needs as per Salesforce documentation.
References:
* Salesforce Agentforce Documentation: Agentforce for Sales > Create Close Plan - Details AI- generated close plans.
* Trailhead: Explore Agentforce Sales Agents - Highlights close plan generation for sales reps.
* Salesforce Help: Sales Features in Agentforce - Confirms focus on deal closure.


質問 # 62
Agentforce は、セットアップで Einstein Generative AI をオンにしました。現在、Agentforce スペシャリストは、プロンプト ビルダーでカスタム プロンプト テンプレートを作成したいと考えています。ただし、セットアップ メニューでプロンプト ビルダーにアクセスできません。
問題の原因は何ですか?

  • A. プロンプト テンプレート ユーザー権限セットが正しく割り当てられませんでした。
  • B. プロンプト テンプレート マネージャーの権限セットが正しく割り当てられませんでした。
  • C. Data Cloud で大規模言語モデル (LLM) が正しく構成されていません。

正解：B

解説：
In order to access and create custom prompt templates inPrompt Builder, the Agentforce Specialist must have thePrompt Template Managerpermission set assigned. Without this permission, they will not be able to accessPrompt Builderin the Setup menu, even thoughEinstein Generative AIis enabled.
* Option Bis correct because thePrompt Template Managerpermission set is required to usePrompt Builder.
* Option A(Prompt Template User permission set) is incorrect because this permission allows users to use prompts, but not create or manage them.
* Option C(LLM configuration in Data Cloud) is unrelated to the ability to accessPrompt Builder.
:
Salesforce Prompt Builder Permissions:https://help.salesforce.com/s/articleView?id=sf.
prompt_builder_permissions.htm


質問 # 63
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