Agentforce-Specialist日本語試験問題集を試そう！ベストAgentforce-Specialist日本語試験問題トレーニングを提供しています [Q53-Q76]

Agentforce-Specialist日本語試験問題集を試そう！ベストAgentforce-Specialist日本語試験問題トレーニングを提供しています

実践サンプルと問題集指導には2026年最新のAgentforce-Specialist日本語有効なテスト問題集

質問 # 53
Universal Containers (UC) は、ナレッジ記事を使用して Agentforce データ ライブラリを構成しました。Agent Builder と Experience Cloud サイトでテストすると、エージェントはグラウンデッド ナレッジ記事情報で応答しません。ただし、Prompt Builder でテストすると、応答が正しく返されます。UC は、この問題のトラブルシューティングを行うために何をすべきでしょうか。

• A. 「ナレッジの管理」を割り当てる新しい権限セットを作成し、それを Agentforce サービス エージェント ユーザーに割り当てます。
• B. 割り当てられたユーザー権限セットに、ナレッジ記事へのアクセスに使用されるプロンプト テンプレートへのアクセスが含まれていることを確認します。
• C. Data Cloud ユーザーの権限セットが Agentforce サービス エージェント ユーザーに割り当てられていることを確認します。

正解：C

解説：
UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let's troubleshoot the issue.
Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.
Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.Prompt templates in Prompt Builder don't require specific permissions beyond general Einstein Generative AI access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent's runtime access, not the template itself, making this incorrect.
Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.
When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.
Why Option C is Correct:
The Agentforce Service Agent User's lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Data Library Setup > Permissions - Requires Data Cloud access for agents.
Trailhead: Ground Your Agentforce Prompts - Notes Data Cloud User permission for Knowledge grounding.
Salesforce Help: Agentforce Security > Agent User Setup - Lists required permission sets.

質問 # 54
モデルプレイグラウンドでは、既存の
Salesforce 対応の基本モデルは Agentforce によって変更できますか?

• A. 温度、Top-kサンプリング、プレゼンスペナルティ
• B. 温度、周波数ペナルティ、出力トークン
• C. 温度、頻度ペナルティ、存在ペナルティ

正解：C

解説：
In Model Playground, An Agentforce working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
* Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
* Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
* Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce's official documentation on foundational model adjustments.

質問 # 55
Universal Containers は、新しい Agentforce サービス エージェントを会社の Web サイトに導入しましたが、会社の Salesforce ナレッジ記事に記載されている顧客の質問に Agentforce サービス エージェントが回答していないというフィードバックを受けています。考えられる問題は何でしょうか?

• A. Agentforce サービス エージェント ユーザーに正しいエージェント タイプ ライセンスが割り当てられていません。
• B. Agentforce サービス エージェント ユーザーに「ナレッジの表示を許可」権限セットが付与されていません。
• C. Agentforce サービス エージェント ユーザーは、標準のエージェント ナレッジ プロファイルの下に作成する必要があります。

正解：B

解説：
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.

質問 # 56
AI スペシャリストは、営業チーム用のプロンプト テンプレートを作成する任務を負っています。テンプレートでは、特定のアカウントに関連するすべての商談の概要を生成する必要があります。
プロンプト テンプレートに関連機会リストのデータを含めるために、AI スペシャリストが使用する必要があるグラウンディング手法はどれですか?

• A. マージ フィールドを使用して、商談のデフォルトの関連リストを参照します。
• B. マージ フィールドを使用して、商談のカスタム関連リストを参照します。
• C. 数式フィールドを使用して、Einstein 関連商談リストを参照します。

正解：A

解説：
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1 Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta/salesforce_ai
/grounding_data_prompts

質問 # 57
Coral Cloud Resorts（CCR）は、顧客の会員レベルが「プレミアム」または「エリート」の場合にのみ予約アクションが利用できるようにエージェントを設定したいと考えています。このビジネスルールは確定的に適用する必要があります。
CCR は何を実施すべきでしょうか?

• A. 顧客のメンバーシップ ティア フィールドにマップされたコンテキスト変数を作成し、MembershipTier に条件付きフィルターを追加します。
• B. 資格のない顧客が予約を完了できないように、基礎となる予約オブジェクトにカスタム検証ルールを設定します。
• C. 予約アクションはプレミアムまたはエリート顧客のみに使用する必要があることを明確に示すトピックの指示を構成し、例を含めます。

正解：A

解説：
Per the AgentForce Configuration and Control Flow Guide, enforcing deterministic business rules-such as restricting certain actions based on a data condition-requires using context variables with conditional filters.
The guide specifies: "Use context variables mapped to relevant Salesforce fields to store state information.
Then apply conditional filters to ensure actions execute only when specific conditions (e.g., membership tier) are met." This ensures the rule is deterministic, meaning the action cannot trigger if the condition is not satisfied.
Option A (object validation rules) restricts record creation or updates but does not control AgentForce's action logic. Option B (topic instructions) relies on natural language guidance, which is non-deterministic and can be ignored by the model.
Therefore, Option C-creating a context variable mapped to the membership tier and applying a conditional filter-is the correct, documented approach.
References (AgentForce Documents / Study Guide):
AgentForce Implementation Guide: "Conditional Logic Using Context Variables" AgentForce Study Guide: "Deterministic Action Control with Filters" Salesforce Agent Configuration Best Practices

質問 # 58
Universal Containers の営業チームは、全国の見込み客と多数のビデオ営業通話を行っています。営業管理部門は、取引条件や顧客の感情などの重要な情報を簡単に理解できる方法を求めています。
このリクエストに対して、An Agentforce はどの Einstein Generative AI 機能を推奨すべきでしょうか?

• A. アインシュタイン会話インサイト
• B. アインシュタイン通話概要
• C. アインシュタインビデオKPI

正解：B

解説：
Einstein Call Summaries is the best option for this scenario because it leverages Salesforce's AI capabilities to automatically summarize key details of video or voice calls. It includes details like deal terms, customer sentiments, follow-up tasks, and other crucial information. This feature is designed to help sales teams focus on their strategies rather than taking extensive manual notes during conversations.
* Einstein Call Summaries: Automatically generates summaries for calls, identifying critical points such as next steps and follow-ups, enhancing efficiency and understanding of deal progression.
* Einstein Conversation Insights: While it provides insights into customer sentiment and engagement, it is more suited for analyzing patterns across conversations rather than summarizing specific call details.
* Einstein Video KPI: Focuses on analyzing key performance indicators within video calls but does not offer summarization features needed for deal terms or sentiment tracking.
This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.

質問 # 59
Universal Containers (UC) は、次の機能を備えた AI 搭載のカスタマー サービス エージェントを実装したいと考えています。
* PDF として保存されている独自のポリシー ドキュメントを取得します。
* 回答が一般的な LLM の知識ではなく、承認された企業データに基づいていることを確認します。UC はまず何をすべきでしょうか?

• A. AI エージェントのスコープを拡張して、すべての Salesforce レコードを検索します。
• B. コンテンツをファイルに追加し、データ ライブラリ オプションを選択します。
• C. ポリシー文書の AI 検索用に Agentforce データ ライブラリを設定します。

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
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)

質問 # 60
カスタム リトリーバー構成でフィルターを適用する目的は何ですか?

• A. フィルターは複数のドキュメントを再フォーマットして 1 つのサマリー出力に集約し、リトリーバーの出力を合理化して統合することで、より効率的で正確な AI グラウンディングを実現します。
• B. フィルターは、検索インデックスで定義されたフィールドに基づいて最大 10 個の条件を適用することで検索結果を絞り込み、返されるコンテンツの関連性を高めます。
• C. フィルターは、検索インデックス内の機密フィールドを自動的に暗号化およびマスクし、パブリッククエリに対して機密でない情報のみが取得されるようにします。

正解：B

解説：
The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.
For instance, an AgentForce retriever could be configured to include only documents tagged as "Active" or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.
Option B is incorrect because filters do not handle encryption or masking of sensitive data - those functions are managed through Data Cloud security and access controls. Option C is incorrect because retrievers do not aggregate or summarize documents; they retrieve data for grounding, leaving summarization to the LLM reasoning layer.
Therefore, the correct answer is Option A - Filters narrow search results using field-based conditions to improve relevancy and retrieval precision.
Reference: AgentForce Implementation Guide - "Configuring Filters for Targeted Retrieval in Custom Retriever Settings."

質問 # 61
Universal Containers は、部門の効率性を向上させるためにエージェントを割り当てたいと考えています。
適切なタスクが適切なエージェントによって処理されることを保証する構成はどれですか?

• A. リード選別のための SDR エージェント、サポート チケットのためのサービス エージェント、HR リクエストのための従業員エージェント
• B. 1つのサービスエージェントで各シナリオを効率的に処理し、サポートに必要なエージェントタイプの数を削減します。
• C. リードとサービス エージェントの HR リクエスト、およびケースが確実に利用できるようにするためのサポート チケットのセールス コーチ エージェント

正解：A

解説：
According to the AgentForce Product Overview and Deployment Guide, Salesforce recommends using purpose-built agents to maximize efficiency across departments. The documentation states:
"Each AgentForce agent type is optimized for a specific function - SDR Agent for sales development and lead nurturing, Service Agent for customer service and support cases, and Employee Agent for internal HR, IT, and productivity tasks." This separation ensures that each team benefits from a domain-specific agent equipped with the correct data access and actions.
Option B incorrectly assigns agent types to mismatched use cases, and Option C reduces efficiency and control by using a single generic agent for multiple domains, which goes against Salesforce's modular AI design principle.
Thus, Option A best aligns with Salesforce's guidance for role-based AgentForce deployment.
References (AgentForce Documents / Study Guide):
AgentForce Product Overview: "Agent Types and Use Cases"
AgentForce Implementation Guide: "Aligning Agents to Departmental Functions" AgentForce Study Guide: "Optimizing Team Efficiency with Specialized Agents"

質問 # 62
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. 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
• C. 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。

正解：B

解説：
Comprehensive and Detailed In-Depth Explanation:
In Salesforce Agentforce, aData Libraryis 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 areindexedto 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 withData 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 # 63
オプションを 1 つ選択します。
Agentforce スペシャリストは、テキスト ブロックから顧客の名前、電話番号、ケース番号のみを抽出するプロンプト テンプレートを作成する必要があります。
大規模言語モデル (LLM) に余分な会話やテキストが含まれないようにするには、Agentforce スペシャリストはどのようにプロンプ​​トを構成する必要がありますか?

• A. 明確に定義された出力指示を使用し、必要な出力例を提供します。
• B. プロンプトで、LLM が応答で名前と値のペアのみを使用するように指示されていることを確認します。
• C. LLM にテキスト内の重要な情報のみを抽出して出力するように指示します。

正解：A

解説：
According to the official AgentForce Prompt Template Design Guide, when extracting specific data such as customer name, phone number, and case number from unstructured text, the best practice is to use well- defined output instructions and examples. The documentation specifies: "To ensure the LLM produces consistent and precise outputs, prompts must include explicit output formatting instructions and examples that demonstrate the desired structure." AgentForce guidance emphasizes structured output control to prevent the LLM from adding conversational or extraneous text. It states: "Always define your output schema clearly - for example, specify JSON or key- value pairs - and provide one or more examples of what the model should return. This ensures the model responds only with structured data and not natural language." Option A ("Ask the LLM to extract and only output important information") is too vague and can still produce variable or verbose responses. Option C ("Ensure the LLM has been told to only use name value pairs") is partially correct but incomplete without clear formatting and example output. Therefore, Option B is the correct choice as it aligns with AgentForce's documented standards for prompt accuracy and reliability.
References (AgentForce Documents / Study Guide):
* AgentForce Prompt Engineering Best Practices Guide
* AgentForce Developer Study Guide: "Defining Structured Outputs in Prompt Templates"
* AgentForce Technical Documentation: "Using Output Instructions and Examples for LLM Control"

質問 # 64
Einstein Trust Layer のどの機能が、ジェイルブレイクやプロンプトインジェクション攻撃のリスクを最小限に抑えるのに役立ちますか?

• A. 安全なデータ取得とグラウンディング
• B. データマスキング
• C. 迅速な防御

正解：C

解説：
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
In contrast:
* Toxicity Scoring (A) identifies harmful or inappropriate content in outputs but does not address data privacy.
* Prompt Defense (C) guards against malicious prompts or injection attacks but focuses on security rather than data protection compliance.

質問 # 65
Universal Containers (UC) は、次の機能を備えた AI 搭載のカスタマー サービス エージェントを実装したいと考えています。
* PDF として保存されている独自のポリシー ドキュメントを取得します。
* 回答が一般的な LLM の知識ではなく、承認された企業データに基づいていることを確認します。UC はまず何をすべきでしょうか?

• A. AI エージェントのスコープを拡張して、すべての Salesforce レコードを検索します。
• B. コンテンツをファイルに追加し、データ ライブラリ オプションを選択します。
• C. ポリシー文書の AI 検索用に Agentforce データ ライブラリを設定します。

正解：C

解説：
Comprehensive and Detailed In-Depth Explanation:
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. TheAgentforce 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)

質問 # 66
Universal Containers のマーケティング チームは、顧客の行動、好み、購入履歴に基づいて電子メールをパーソナライズする方法を模索しています。
チームがソリューションとしてエージェントを使用する必要があるのはなぜですか?

• A. すべての顧客に自動メールを送信する
• B. 各顧客と関わる際に関連性の高いコンテンツを生成する
• C. 過去のキャンペーンのパフォーマンスを分析する

正解：B

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

質問 # 67
AI スペシャリストは、営業チーム用のプロンプト テンプレートを作成する任務を負っています。テンプレートでは、特定のアカウントに関連するすべての商談の概要を生成する必要があります。
プロンプト テンプレートに関連機会リストのデータを含めるために、AI スペシャリストが使用する必要があるグラウンディング手法はどれですか?

• A. マージ フィールドを使用して、商談のデフォルトの関連リストを参照します。
• B. マージ フィールドを使用して、商談のカスタム関連リストを参照します。
• C. 数式フィールドを使用して、Einstein 関連商談リストを参照します。

正解：A

解説：
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
* Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1
* Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta
/salesforce_ai/grounding_data_prompts

質問 # 68
管理者は、Universal Containers (UC) の CRM データのセキュリティと信頼性を確保する責任があります。UC には、強化されたデータ保護と最新の AI 機能が必要です。UC には、プロンプトとマージする Salesforce レコードからの関連情報も含める必要があります。
Einstein Trust Layer のどの機能が UC のニーズを最もよくサポートしますか?

• A. ゼロデータ保持ポリシー
• B. データマスキング
• C. 安全なデータ取得による動的グラウンディング

正解：C

解説：
Dynamic grounding with secure data retrieval is a key feature in Salesforce'sEinstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allowsrelevant Salesforce datato be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.
Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model's outputs are trustworthy and reliable for business use.
The other options are less aligned with the requirement:
* Data maskingrefers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.
* Zero-data retention policyensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.
:
Salesforce Developer Documentation onEinstein Trust Layer
Salesforce Security Documentation for AI andData Privacy

質問 # 69
Universal Containers (UC) は、業務効率の向上を目指しています。UC は最近 Salesforce を導入し、プロセスを改善するために Agent の実装を検討しています。
エージェントを実装する主な理由は何ですか?

• A. ワークフローの合理化と反復タスクの自動化
• B. ユーザーの操作なしで AI がタスクを実行できるようにする
• C. データ入力とデータクレンジングの改善

正解：A

解説：
The key reason for implementing Agent is its ability to streamline workflows and automate repetitive tasks
. By leveraging AI, Agent can assist users in handling mundane, repetitive processes, such as automatically generating insights, completing actions, and guiding users through complex processes, all of which significantly improve operational efficiency.
* Option A (Improving data entry and cleansing) is not the primary purpose of Agent, as its focus is on guiding and assisting users through workflows.
* Option B (Allowing AI to perform tasks without user interaction) does not accurately describe the role of Agent, which operates interactively to assist users in real time.
Salesforce Agentforce Specialist References:More details can be found in the Salesforce documentation:
https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_overview.htm

質問 # 70
Universal Containers では、音声通話とビデオ通話の記録を分析して、競合他社の言及、コーチングの機会、その他の重要な情報に関する洞察を提供できるツールを必要としています。目標は、改善の余地と競合情報を特定することで、チームのパフォーマンスを向上させることです。
競合他社の言及やコーチングの機会に関する洞察を提供する機能はどれですか?

• A. エクスプローラーを呼び出す
• B. アインシュタインセールスインサイト
• C. 通話要約

正解：A

解説：
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
* Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
* Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.
References:
* Salesforce Einstein Conversation Insights Documentation: https://help.salesforce.com/s/articleView?
id=einstein_conversation_insights.htm

質問 # 71
ナレッジベース データ ライブラリ構成において、識別フィールドとコンテンツ フィールドの主な違いは何ですか?

• A. 識別フィールドは正しいナレッジ記事を見つけるのに役立ち、コンテンツ フィールドは AI 応答に詳細情報を追加します。
• B. 識別フィールドは関連性スコアリングのためのキーワードを強調表示し、コンテンツ フィールドには検索用に記事の全文を保存します。
• C. 識別フィールドはインデックス作成の目的で記事を分類し、コンテンツ フィールドは表示用の簡単な概要を提供します。

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation:In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let's analyze their roles.
* Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.In a Knowledge-based data library, identifying fields (e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context. Content fields (e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce's documentation on Knowledge integration, making it the correct answer.
* Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.Identifying fields do more than categorize-they actively locate articles, not just index them. Content fields aren't limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
* Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:The primary difference-identifying fields for locating articles and content fields for enriching responses-reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
References:
* Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup - Defines identifying vs. content fields.
* Trailhead: Ground Your Agentforce Prompts - Explains field roles in Knowledge integration.
* Salesforce Help: Knowledge in Agentforce - Confirms locating and enriching functions.

質問 # 72
エージェントアクションの実行における大規模言語モデル (LLM) の役割は何ですか?

• A. 類似のリクエストを検索し、実行する必要があるアクションを提供します
• B. ユーザーのアクセスを決定し、実行するアクションを優先度順に並べ替えます
• C. 最も一致するアクションと正しい実行順序を特定する

正解：C

解説：
In Agent, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user's request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Agent Actions
Salesforce AI Documentation on Large Language Models

質問 # 73
Agentforce States Agent を営業ユーザーに導入して成功した後、Universal Containers はこれをサービス チームに導入することを目指しています。
Agentforce スペシャリストがこの展開で留意すべき重要な考慮事項は何ですか?

• A. Service Center のユースケースの標準およびカスタムのエージェント トピックとアクションを確認してテストします。
• B. 標準サービスアクションを Agentforce Service Agent に割り当てます。
• C. Service Cloud ユーザーに Agentforce for Service 権限を割り当てます。

正解：A

解説：
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
* Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
* Option A: Permissions like "Agentforce for Service" are necessary but secondary to functional compatibility.
* Option B: Standard service actions must be mapped to Agentforce, but testing ensures they function as intended.
References:
* Salesforce Help: Einstein Agent Setup
* Emphasizes reviewing "topics and actions for different user groups (Sales vs. Service)."

質問 # 74
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. 一致した商談のステータスは、過去 12 か月間で「受注成立」です。
• 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. 一致する商談は過去 12 か月以内に作成されました。

正解：A

解説：
Comprehensive and Detailed In-Depth Explanation:
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 # 75
Universal Containers には、ビジネス要件を完全に満たしていないアクティブな標準の電子メール プロンプト テンプレートがあります。問題の標準のプロンプト メール テンプレートのコンテンツを使用し、ビジネス要件を完全に満たすようにカスタマイズするには、Agentforce スペシャリストはどのような手順を踏む必要がありますか?

• A. 既存のテンプレートを複製し、必要に応じて変更します。
• B. 新しいバージョンとして保存し、必要に応じて編集します。
• C. 新しいテンプレートとして保存し、必要に応じて編集します。

正解：A

解説：
Universal Containers (UC) has a standard email prompt template (likely a prebuilt template provided by Salesforce) that isn't meeting their needs, and they want to customize it while retaining its original content as a starting point. Let's assess the options based on Agentforce prompt template management practices.
Option A: Save as New Template and edit as needed.In Agentforce Studio's Prompt Builder, there's no explicit "Save as New Template" option for standard templates. This phrasing suggests creating a new template from scratch, but the question specifies using the content of the existing standard template. Without a direct "save as" feature for standards, this option is imprecise and less applicable than cloning.
Option B: Clone the existing template and modify as needed.Salesforce documentation confirms that standard prompt templates (e.g., for email drafting or summarization) can be cloned in Prompt Builder. Cloning creates a custom copy of the standard template, preserving its original content and structure while allowing modifications. The Agentforce Specialist can then edit the cloned template-adjusting instructions, grounding, or output format-to meet UC's specific business requirements. This is the recommended approach for customizing standard templates without altering the original, making it the correct answer.
Option C: Save as New Version and edit as needed.Prompt Builder supports versioning for custom templates, allowing users to save new versions of an existing template to track changes. However, standard templates are typically read-only and cannot be versioned directly-versioning applies to custom templates after cloning.
The question implies starting with the standard template's content, so cloning precedes versioning. This option is a secondary step, not the initial action, making it incorrect.
Why Option B is Correct:
Cloning is the documented method to repurpose a standard prompt template's content while enabling customization. After cloning, the specialist can modify the new custom template (e.g., tweak the email prompt's tone, structure, or grounding) to align with UC's requirements. This preserves the original standard template and follows Salesforce best practices.
References:
Salesforce Agentforce Documentation: Prompt Builder > Managing Templates - Details cloning standard templates for customization.
Trailhead: Build Prompt Templates in Agentforce - Explains how to clone standard templates to create editable copies.
Salesforce Help: Customize Standard Prompt Templates - Recommends cloning as the first step for modifying prebuilt templates.

質問 # 76
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