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質問 # 122
Universal Containers (UC) は、Flow を使用して、統合された Data Cloud オブジェクトからプロンプト テンプレートにデータを取り込むことを望んでいます。
UC ではどのタイプのフローを使用すればよいですか?
- A. 統合オブジェクトリンクフロー
- B. データクラウドがトリガーするフロー
- C. テンプレートによってトリガーされるプロンプトフロー
正解：C
解説：
In this scenario, Universal Containers wants to bring data from unified Data Cloud objects into prompt templates, and the best way to do that is through a Data Cloud-triggered flow. This type of flow is specifically designed to trigger actions based on data changes within Salesforce Data Cloud objects.
Data Cloud-triggered flows can listen for changes in the unified data model and automatically bring relevant data into the system, making it available for prompt templates. This ensures that the data is both real-time and up-to-date when used in generative AI contexts.
For more detailed guidance, refer to Salesforce documentation on Data Cloud-triggered flows and Data Cloud integrations with generative AI solutions.
質問 # 123
Universal Containers は、チームが Slack チャンネルでエージェントと直接やり取りできるように、Slack で Agentforce を有効にする予定です。
Slack で Agentforce を有効にするために必要な主な手順を表す説明はどれですか?
- A. Slackワークフローを設定してAgentforce APIを呼び出すことで、ユーザーは定義済みのトリガーと自動化されたステップを通じてエージェントと対話できるようになります。
- B. Slack エージェント接続を構成し、Manage Agentforce でエージェントをインストールし、ユーザーにエージェント アクセスを割り当てます。
- C. デフォルトの Slack チャネル Agentforce を有効にし、ユーザーに Slack エージェント アクセスを割り当てます。
正解：B
解説：
The AgentForce for Slack Deployment Guide outlines the exact process for enabling AgentForce in Slack.
The steps include:
* Configuring the Slack agent connection to link Salesforce with the Slack workspace.
* Installing the agent in the "Manage AgentForce" section.
* Assigning agent access to specific Slack users or channels.
The documentation notes: "Administrators must first establish a Slack connection through Salesforce setup, then deploy the AgentForce app to the desired workspace. User permissions are managed in the Manage AgentForce console." Option A is incorrect because there is no "default Slack channel AgentForce." Option B refers to Slack workflows, which are unrelated to direct agent configuration.
Therefore, Option C accurately describes the official Salesforce method for enabling AgentForce in Slack.
References (AgentForce Documents / Study Guide):
* AgentForce for Slack Integration Guide: "Steps to Connect and Deploy Agents"
* Salesforce Setup for AgentForce Collaboration Platforms
* AgentForce Study Guide: "Configuring Slack Agent Connections and User Access"
質問 # 124
Agentforce スペシャリストが Agentforce でカスタム アクションを作成しています。Agentforce スペシャリストがカスタム エージェント アクションに選択できるオプションはどれですか?
- A. SOQL
- B. Flows
- C. Apex Trigger
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:The Agentforce Specialist is defining a custom action for an Agentforce agent in Agent Builder. Actions determine what the agent does (e.g., retrieve data, update records). Let's evaluate the options.
* Option A: Apex TriggerApex Triggers are event-driven scripts, not selectable actions in Agent Builder. While Apex can be invoked via other means (e.g., Flows), it's not a direct option for custom agent actions, making this incorrect.
* Option B: SOQLSOQL (Salesforce Object Query Language) is a query language, not an executable action type in Agent Builder. While actions can use queries internally, SOQL isn't a standalone option, making this incorrect.
* Option C: FlowsIn Agentforce Studio's Agent Builder, custom actions can be created using Salesforce Flows. Flows allow complex logic (e.g., data retrieval, updates, or integrations) and are explicitly supported as a custom action type. The specialist can select an existing Flow or create one, making this the correct answer.
* Option D: JavaScriptJavaScript isn't an option for defining agent actions in Agent Builder. It's used in Lightning Web Components, not agent configuration, making this incorrect.
Why Option C is Correct:Flows are a native, flexible option for custom actions in Agentforce, enabling tailored functionality for agents, as per official documentation.
References:
* Salesforce Agentforce Documentation: Agent Builder > Custom Actions - Lists Flows as a supported action type.
* Trailhead: Build Agents with Agentforce - Details Flow-based actions.
* Salesforce Help: Configure Agent Actions - Confirms Flows integration.
質問 # 125
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.
References:
* Salesforce Agent Overview: https://help.salesforce.com/s/articleView?id=einstein_copilot_overview.
htm
質問 # 126
Universal Containers (UC) は、Sales Development Representative (SDR) エージェントを実装したいと考えています。
UC を実装する際には、どのチャネルの考慮事項に注意する必要がありますか?
- A. SDR エージェントは会社の Web サイトにも展開する必要があります。
- B. SDR エージェントはメッセージング チャネルに展開する必要があります。
- C. SDR エージェントは電子メール チャネルでのみ動作します。
正解：B
解説：
Comprehensive and Detailed In-Depth Explanation:
Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment. Let's evaluate the options based on official Salesforce documentation.
* Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is designed to engage prospects in real-time conversations, primarily through the Messaging channel (e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making this a key consideration UC must be aware of. This is the correct answer.
* Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email.
While it can send emails (e.g., follow-ups after lead qualification), its primary function-real-time lead engagement-relies on Messaging. Stating it "only works in the Email channel" is inaccurate and contradicts its documented capabilities, making this incorrect.
* Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation choice, not a mandatory requirement. The agent's deployment is channel-specific (Messaging), and website integration is optional, not a "must." This option overstates the requirement, making it incorrect.
Why Option A is Correct:
The SDR Agent's primary deployment in the Messaging channel is a documented consideration for its real- time lead qualification capabilities. UC must plan for this channel to ensure effective implementation, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: SDR Agent Setup > Channels- Specifies Messaging as the primary channel.
Trailhead: Explore Agentforce Sales Agents- Notes SDR Agent's Messaging focus for lead engagement.
Salesforce Help: Agentforce for Sales > SDR Agent- Confirms Messaging deployment requirement.
質問 # 127
Universal Containers は、大規模言語モデル (LLM) によって生成されたコンテンツに有害な言語が含まれているかどうかを高い信頼性で検出できるようにしたいと考えています。
毒性が適切に管理されていることを確認するために、AI スペシャリストは Trust レイヤーでどのようなアクションを実行する必要がありますか?
- A. 応答からの毒性スコアが事前定義されたしきい値を超えるたびに、指定されたアドレスに電子メールを送信するフローを作成します。
- B. セットアップで毒性検出ログにアクセスし、isToxicityDetected が true であるすべてのエントリをエクスポートします。
- C. 毒性検出器タイプのフィルターを使用して毒性応答とそれぞれのスコアを表示する、Data Cloud 内で Trust Layer 監査レポートを作成します。
正解：C
解説：
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the Agentforce Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.
* Option C is correct because it enables visibility into toxic language detection within the Trust Layer and allows for auditing responses for toxicity.
* Option A suggests checking a toxicity detection log, but Salesforce provides more comprehensive options via the audit report.
* Option B involves creating a flow, which is unnecessary for toxicity detection monitoring.
References:
* Salesforce Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
質問 # 128
企業が Agentforce の Data Cloud でハイブリッド検索インデックスを好む理由を説明している記述はどれですか。
- A. ハイブリッド検索インデックスでは、セマンティック埋め込みが不要になるため、ベクター検索よりもクエリを高速に処理します。
- B. ハイブリッド検索でのベクトル埋め込みはキーワードの一致によって事前にフィルタリングされるため、計算オーバーヘッドが削減され、応答の精度が向上します。
- C. ハイブリッド検索インデックスは、リテラルキーワード一致とセマンティックリコールの両方をサポートしており、クエリに特定の用語と意図が混在している場合に役立ちます。
正解：C
解説：
According to the AgentForce Data Cloud Search Indexing Guide and RAG Optimization Framework, a hybrid search index combines both keyword-based (lexical) and vector-based (semantic) search capabilities. This dual-mode retrieval enables AgentForce to interpret user intent while still honoring exact keyword matches.
In many enterprise scenarios, queries contain a mixture of specific terms (e.g., "contract ID 54321") and semantic intent (e.g., "renew my subscription"). A purely vector search might overlook exact keywords, while a keyword-only search might miss semantically relevant results. Hybrid indexing ensures that both types of retrieval are available simultaneously - providing the best balance of precision and contextual understanding.
Option A is incorrect because hybrid search still uses embeddings; it doesn't eliminate them. Option B partially describes the hybrid search process but oversimplifies its purpose - the primary goal isn't just prefiltering for performance, but combining semantic recall and exact matching for more relevant, balanced results.
Thus, per AgentForce documentation, hybrid search indexes are preferred when organizations need both literal keyword matching and semantic understanding for complex, natural-language queries.
Reference: AgentForce Data Cloud Documentation - "Hybrid Search Index: Combining Keyword and Semantic Retrieval."
質問 # 129
エージェントがトピックを選択した後、推論エンジンがアクションを選択するために使用する重要な要素は何ですか?
- A. アクションの名前と説明
- B. 各アクションに与えられる優先度
- C. トピック内のアクションの明示的な順序
正解：B
解説：
The most crucial factor a reasoning engine uses to select an action after a topic is chosen is the priority given to each action (A). In advanced agent frameworks like AgentForce (simulated context), actions within a topic are typically not executed simply in an explicit, fixed order () unless there's no conditional logic. Instead, the reasoning engine evaluates all available actions and their associated pre-conditions (or triggers) and priorities.
A priority score is often a numerical value assigned to an action that dictates its relative importance when multiple actions could potentially be executed simultaneously or when the agent must choose the 'best' action to address the current topic state. This prioritization ensures the agent handles the most critical or relevant tasks first, which is essential for efficient and goal-oriented behavior. The action's name and instructions () are descriptive for the developer but are not the primary selection criteria used by the runtime reasoning engine itself; it's the logic and priority that govern execution.
Simulated Exact Extract of AgentForce documents (Conceptual Reference):
"Once a Topic is selected, the Reasoning Engine iterates through the associated Actions. The primary mechanism for action selection is the evaluation of the Action Priority level, in conjunction with satisfied pre- conditions. Actions with a higher priority value will be given preference for execution, overriding any simple sequential order unless a fixed pipeline is explicitly enforced. This ensures the agent is consistently performing the most relevant or time-sensitive task for the active topic." Simulated Reference: AgentForce Study Guide, Chapter 4: Reasoning Engine and Action Prioritization, p. 78.
質問 # 130
オプションを 1 つ選択します。
Universal Containers は従業員エージェントを作成しました。
エージェントを Slack チャネルに接続するために、Agentforce スペシャリストが実行する必要がある手順はどれですか?
- A. Salesforce と Slack ワークスペース間の埋め込みサービスデプロイメントと接続を作成します。
- B. Salesforce と Slack ワークスペース間のオムニチャネル フローと接続を作成します。
- C. Salesforce と Slack ワークスペース間の接続を作成します。
正解：C
解説：
According to the AgentForce for Slack Integration Guide, to connect an Employee Agent (or any internal AgentForce agent) with a Slack channel, the required setup step is to create a connection between Salesforce and the Slack workspace. The documentation specifies: "Before deploying an Employee Agent into Slack, you must establish a secure connection between your Salesforce org and the Slack workspace. This connection enables authentication, permission mapping, and message exchange between the Agent and Slack users." Once the connection is established, the administrator can configure the specific Slack channel where the agent will operate.
Option B, involving Omni-Channel flow, applies to Salesforce Service or Support routing, not Slack integration. Option C, Embedded Service Deployment, is used for web or mobile integrations, not Slack.
Therefore, Option A accurately aligns with AgentForce's official integration framework for Slack connectivity.
References (AgentForce Documents / Study Guide):
AgentForce for Slack Integration Guide: "Connecting Salesforce and Slack Workspaces" AgentForce Employee Agent Setup Notes Salesforce AgentForce Study Guide: "Deploying Agents into Collaboration Platforms"
質問 # 131
Universal Containers は、チャットでよくある質問に対する定型的な回答を入力する時間を最小限に抑え、ケース フィールドの値を提案することでチャット後の分析を減らすことで、全体的な顧客サポート処理時間を短縮したいと考えています。この取り組みを可能にするのは、Agentforce for Service 機能のどの組み合わせですか。
- A. アインシュタインの返信推奨とケース分類
- B. Einstein サービスの返信と作業概要
- C. アインシュタインの返信推奨事項とケース概要
正解：C
解説：
Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service, Einstein Reply Recommendations and Case Classification (Option A) are the ideal combination to achieve this.
Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.
Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.
Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.
Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.
Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).
Thus, Option A is the correct answer for UC's needs.
Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5) Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help: https://help.salesforce.com/s
/articleView?id=sf.case_classification.htm&type=5)
Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce-for- service)
質問 # 132
Universal Containers は、ユーザーが構造化クエリ (モデル番号) と自然言語の質問 (たとえば、「デバイスをリセットするにはどうすればよいですか?」) の両方を尋ねる可能性がある数百万の製品マニュアルのインデックスを作成していますが、同社はどちらの検索アプローチを使用すべきでしょうか?
- A. モデル番号がクエリの大部分を占めるため、キーワード検索のみを使用します。
- B. ハイブリッド検索を使用して、キーワードの精度と意味の柔軟性を組み合わせます。
- C. 自然言語が常に優先されるため、セマンティック検索のみを使用します。
正解：B
解説：
According to the AgentForce Retrieval Optimization Guide, when users ask both structured (exact) and unstructured (natural language) questions, the best practice is to use hybrid search. The documentation states:
"Hybrid search combines the precision of keyword retrieval for structured terms, such as IDs or model numbers, with the semantic flexibility of vector search for natural language queries. This approach ensures both deterministic accuracy and contextual understanding." Option A (keyword search only) fails for natural language queries, which require semantic understanding.
Option B (semantic search only) may misinterpret or overlook structured identifiers like product or model numbers.
Therefore, Option C-hybrid search-provides the ideal balance between exact match precision and contextual recall.
References (AgentForce Documents / Study Guide):
AgentForce Retrieval and Indexing Guide: "Choosing Between Keyword, Semantic, and Hybrid Search" AgentForce Data Cloud Handbook: "Optimizing Multi-Modal Retrieval Strategies" AgentForce Study Guide: "Hybrid Search for Structured and Unstructured Queries"
質問 # 133
プロンプト テンプレートを構成するとき、Agentforce スペシャリストは、作成したプロンプト テンプレートの結果をプレビューします。2 つの異なるテキスト出力 (解決と応答) が表示されます。解決テキストはどのような情報を提供しますか?
- A. サンプルレコードに基づく LLM からの応答を表示します。
- B. LLM に送信される前にマスクされる機密データを示します。
- C. 信頼レイヤーに送信される全文を表示します。
正解：A
解説：
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs: Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
Option B: The "Response" output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response-it includes the entire payload sent to the Trust Layer.
Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate "which sensitive data is masked." Instead, it shows the full text, including any masked portions, as processed by the Trust Layer-not a separate masking log.
Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.
Salesforce Agentforce Documentation: "Preview Prompt Templates" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_prompt_preview.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Trust Layer Outputs" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)
質問 # 134
Universal Containers (UC) は、AI で生成されたケース サマリーを使用してエージェントの時間を節約する必要があります。UC は作業サマリー機能を実装しました。
アインシュタインは要約を生成する際に何を考慮しますか?
- A. 生成は既存の会話コンテキストのみに基づいて行われます。
- B. 生成は会話のコンテキストとナレッジ記事に基づいています。
- C. 生成は会話のコンテキスト、ナレッジ記事、ケースに基づいています。
正解：C
解説：
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
* Conversation Context:
* Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
* Knowledge Articles:
* It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
* Cases:
* Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
* Option A is correct as it includes all three: conversation context, Knowledge articles, and cases.
* Option B is incorrect because it limits the grounding to conversation context only, excluding other critical elements.
* Option C is incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.
質問 # 135
Einstein Studio でカスタム リトリーバーを作成する場合、どの手順が必須と考えられますか?
- A. 検索インデックスを構成し、ベクター検索またはハイブリッド検索を選択し、フィルタリングするフィールド、データ空間、モデルを選択してから、ランキング方法を定義します。
- B. 返される結果の最大数を指定して出力構成を定義し、プロンプトの基礎となる出力フィールドをマップします。
- C. 検索インデックスを選択し、関連付けられたデータ モデル オブジェクト (DMO) とデータ スペースを指定し、必要に応じてフィルターを定義して検索結果を絞り込みます。
正解：C
解説：
Comprehensive and Detailed In-Depth Explanation:In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
* Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
* Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
* Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
* Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.
* Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
* Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
References:
* Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
* Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/einstein-studio-for-agentforce)
質問 # 136
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. 通話要約
- B. エクスプローラーを呼び出す
- C. アインシュタインセールスインサイト
正解：B
解説：
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information,Call Exploreris the most suitable feature.Call Explorer, a part ofEinstein 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 mentionsand moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
* Call Summariesoffer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
* Einstein Sales Insightsfocuses more on pipeline and forecasting insights rather than call-based analysis.
:
Salesforce Einstein Conversation Insights Documentation:https://help.salesforce.com/s/articleView?
id=einstein_conversation_insights.htm
質問 # 137
ユニバーサルコンテナ（UC）は、最新かつ有効な関連性の高いポリシーとコンプライアンス情報を顧客に提供するAgentforceサービスエージェントを構築したいと考えています。エージェントは以下の要件を満たす必要があります。
* 人事ポリシー、コンプライアンス ガイドライン、会社の手順を意味的に検索します。
* 回答が公開されている知識に基づいていることを確認します。
* 手動で再構成することなく、ナレッジの更新をすぐに反映できるようにします。エージェントが正しい情報を取得できるようにするには、UC では何をする必要がありますか?
- A. エージェントがすべての内部記録と過去の顧客からの問い合わせを検索できるようにします。
- B. 幻覚を防ぐために、ポリシー応答を AI モデルに手動で追加します。
- C. AI による検索用にポリシー ドキュメントを保存およびインデックス付けするための 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:
UC requires an Agentforce Service Agent to deliver accurate, up-to-date policy and compliance info with specific criteria. Let's evaluate.
* Option A: Enable the agent to search all internal records and past customer inquiries.Searching all records and inquiries risks irrelevant or outdated responses, conflicting with the need for published Knowledge grounding and immediate updates. This lacks specificity, making it incorrect.
* Option B: Set up an Agentforce Data Library to store and index policy documents for AI retrieval.The Agentforce Data Library integrates with Salesforce Knowledge, indexing HR policies, compliance guidelines, and procedures for semantic search. It ensures grounding in published Knowledge articles, and updates (e.g., new article versions) are reflected instantly without reconfiguration, as the library syncs with Knowledge automatically. This meets all UC requirements, making it the correct answer.
* Option C: Manually add policy responses into the AI model to prevent hallucinations.Manually embedding responses into the model isn't feasible-Agentforce uses pretrained LLMs, not custom training. It also doesn't support real-time updates, making this incorrect.
Why Option B is Correct:
The Data Library meets all criteria-semantic search, Knowledge grounding, and instant updates-per Salesforce's recommended approach.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Integration- Details indexing and updates.
Trailhead: Build Agents with Agentforce- Covers Data Library for accurate responses.
Salesforce Help: Grounding with Knowledge- Confirms real-time sync.
質問 # 138
Prompt Builder の主な目的は何ですか?
- A. Salesforce 内のツールで、ユーザーにリアルタイムの AI を活用した提案やガイダンスを提供し、生産性と意思決定を向上させます。
- B. 企業が大規模言語モデル（LLM）の再利用可能なプロンプトを作成し、ワークフローに生成的なAI応答をもたらすツール
- C. 開発者が Visual Studio Code で使用するツール。Apex プログラミングのプロンプトを作成し、開発者がより効率的にコードを記述できるように支援します。
正解：B
解説：
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
It is not a tool for Apex programming (as in option A).
It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
質問 # 139
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. 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。
- B. 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。
- C. 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。
正解：A
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 # 140
オプションを 1 つ選択します。
Universal Containers (UC) は外部サービスを登録し、テンプレートトリガーのプロンプトフローを作成しました。このフローは、外部サービスを呼び出して REST API からデータを取得します。UC は次に、外部サービスからの応答データを、プロンプトテンプレートの実行時に差し込み項目として使用できるようにする必要があります。
UC はこの要件をどのように満たすべきでしょうか?
- A. 外部サービスレコードのマージ フィールドを使用します。
- B. JSON を XML マージ フィールドに変換します。
- C. 「プロンプト指示の追加」フロー要素を使用します。
正解：A
解説：
As outlined in the AgentForce External Services and Prompt Flow Integration Guide, when data is retrieved from a registered external service via REST API, the response payload is stored as External Service Records.
These records can then be referenced dynamically within prompt templates through External Service Record merge fields.
This approach allows the large language model (LLM) to use the fetched data as contextual grounding during prompt execution, ensuring that generated responses are accurate and consistent with the latest API results.
Option B is incorrect because AgentForce does not use XML merge fields for API responses; JSON data is automatically mapped to object structures. Option C is also incorrect - the "Add Prompt Instructions" element modifies prompt context or tone but does not pass external data for merge use.
Therefore, the correct method is Option A - Use External Service Record merge fields, ensuring the external service data is directly available for prompt templates.
Reference: AgentForce Developer Guide - "Integrating External Services and Using Merge Fields in Prompt Flows."
質問 # 141
オプションを 1 つ選択します。
Coral Cloud Resorts（CCR）は、エージェントが以前に収集した食事やアクティビティの好みを忘れていることに気づきました。セッション中はそれらの好みを維持する必要があります。
CCR は何を実施すべきでしょうか?
- A. アクション出力から顧客の好みをキャプチャ/保存するためのカスタム変数を構成します。
- B. 自然な会話の記憶に頼り、エージェントに振り返るように指示します。
- C. 顧客の好みをアクション出力としてキャプチャ/保存するためのコンテキスト変数を作成します。
正解：C
解説：
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters." By contrast, custom variables (Option A) are typically used for storing intermediate action outputs but are not automatically persistent across the full session. Relying on conversation memory (Option B) alone is non- deterministic and may cause data loss due to memory truncation or token limits.
Thus, Option C - creating a context variable to store and recall customer preferences - aligns with Salesforce's recommended implementation for session-level persistence.
References (AgentForce Documents / Study Guide):
AgentForce Configuration Guide: "Using Context Variables for Session Data" AgentForce Study Guide: "Persistent Memory and Variable Management" AgentForce Implementation Handbook: "Maintaining Context Across User Sessions"
質問 # 142
オプションを 1 つ選択します。
Universal Containers (UC) は、Lightning Web コンポーネントから呼び出すことができるカスタム プロンプト テンプレートを作成する必要があります。
UC はどのタイプのプロンプト テンプレートを作成する必要がありますか?
- A. フレックス
- B. 営業メール
- C. フィールド生成
正解：A
解説：
The AgentForce Developer Integration Guide specifies that Flex prompt templates are the correct type for custom or embedded integrations, such as invoking a prompt from a Lightning Web Component (LWC).
Flex templates are designed for general-purpose use cases and can be called programmatically via Apex, Flow, or LWC APIs. They offer flexible input and output structures, allowing developers to integrate AgentForce reasoning into custom applications and UI components.
Option A, Field Generation, is used to populate or update Salesforce fields, not for external invocation.
Option B, Sales Email, is specific to generating pre-formatted communication messages and cannot be invoked directly from LWCs.
Therefore, the correct template type for a prompt used within a Lightning Web Component is Option C - Flex, as it is purpose-built for dynamic, reusable, and programmatic use cases.
Reference: AgentForce Developer Guide - "Using Flex Prompt Templates with Lightning Web Components."
質問 # 143
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 # 144
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