証明書を効率的に渡す状況を確認するために、当社のAB-731練習資料は一流の専門家によって編集されています。 したがって、私たちのチームの能力は疑う余地がありません。 役に立たないものに貴重な時間を無駄にすることなく、レビューして順調に進むのに役立ちます。 彼らは、最近の試験でAB-731スタディガイドが通常テストするものを厳選し、これらのAB-731実際のテストに蓄積した知識を捧げました。 私たちは同じチームに所属しており、あなたがそれを実現するのを助けることが私たちの共通の願いです。 とても幸運！
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JPTestKing練習資料は、成功するための貴重な可能性を奪います。 このラインのプロのモデル会社として、AB-731トレーニング資料の成功：AI Transformation Leaderは予見できる結果になります。 一部の厳選された顧客でさえ、彼らの高品質と正確さの実践をやめることはできません。 私たちは品質の問題に非妥協的であり、あなたは彼らの習熟度を厳しく完全に確信することができます。 長年の訂正と修正を受けて、AB-731試験問題はすでに完璧になっています。 彼らは、エラーのない有望な練習資料です。 成功への道を示す指標として、私たちの練習資料はあなたの旅のあらゆる困難を乗り越えることができます。 すべての課題をウォークインのように扱うことはできませんが、AB-731シミュレーションの実践により、Microsoftレビューを効果的にすることができます。 それが彼らがラインのプロモデルである理由です。
質問 # 42
- Select the answer that correctly completes the sentence.
The cost of using generative AI language models is based typically on the number of __________ processed.
正解：
解説：
Explanation:
Most generative AI language model pricing is based on token consumption , which measures the amount of text processed by the model. Tokens are sub-word units used internally by language models (for example, parts of words, whole words, or punctuation). When you send a prompt, the model consumes input tokens (your prompt + any system instructions + retrieved grounding context). When it generates a response, it consumes output tokens (the generated completion). Costs typically scale with the total input + output tokens processed, which is why long prompts, large grounding passages, and lengthy responses increase spend. This also explains why prompt optimization, response length limits, caching, and careful grounding are common cost-control techniques in enterprise solutions.
By contrast, "documents" is too coarse (a document can be 1 page or 500 pages). "Requests" is not the primary unit for most LLM pricing models because request sizes vary dramatically. "Words" is not used because the model's actual compute unit is tokens, and tokenization differs across languages and text patterns.
Therefore, the most accurate completion is tokens .
質問 # 43
- For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique: you steer outputs by improving the instructions and context you send to the model. Changing how a model was trained would involve pretraining, fine-tuning, or other training methods-separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions, the right context, and explicit output constraints. This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary: enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
質問 # 44
- Select the answer that correctly completes the sentence.
When you use Microsoft 365 Copilot connectors to connect external content to __________, your users can find, summarize, and learn from line-of-business (LOB) data by using natural language prompts.
正解：
解説：
Explanation:
Microsoft Graph
Microsoft 365 Copilot connectors (built on Microsoft Graph connectors) are used to bring external, line-of- business content into the Microsoft 365 ecosystem by ingesting it into Microsoft Graph . Once connected, the content can be indexed and made discoverable through Microsoft Search and available for Copilot experiences, enabling users to use natural language prompts to find and summarize relevant LOB information-subject to permissions and governance controls.
The other choices don't match how Copilot connectors are positioned. Azure AI Search is an Azure indexing
/retrieval service used in custom RAG solutions, but Microsoft 365 Copilot connectors are specifically designed to surface external content through Microsoft 365 experiences via Graph. Microsoft Purview focuses on data governance, compliance, and risk management rather than being the primary ingestion target for Copilot connector content. SharePoint can store content, but the connector model is about indexing external systems into Microsoft Graph so the content becomes searchable and usable across Microsoft 365, not merely placing it into SharePoint as the destination.
So the correct completion is Microsoft Graph because that is the foundational data and indexing fabric Copilot uses to reason over organizational content with appropriate permission trimming.
質問 # 45
Drag and Drop Question
Match the business scenario to the appropriate AI solution design approach.
To answer, drag the AI solution from the column on the left to its business scenario on the right.
Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正解：
解説：
Explanation:
Box 1: Build with Microsoft Copilot Studio
The marketing department at your company wants AI to summarize emails and create presentations.
Building an AI solution to summarize emails and create presentations using Microsoft Copilot Studio involves creating custom AI agents that leverage your company's Microsoft 365 data (emails, documents) to automate these workflows. Copilot Studio allows you to create, test, and publish conversational agents that can be integrated into Microsoft Teams or directly into Office apps.
Box 2: Use Microsoft 365 Copilot
The HR department at your company wants a conversational agent for policy questions and leave request.
A company can use Microsoft 365 Copilot to create a conversational agent for policy questions and leave requests, primarily by leveraging Copilot Studio to customize agents with specific organizational data. These agents can function within Microsoft Teams, providing a self-service,
24/7 interface for employees.
Box 3: Build with Azure Machine Learning
The manufacturing department at your company wants AI to predict maintenance schedules.
Azure Machine Learning (Azure ML) is a powerful, cloud-based platform that helps companies shift from reactive or scheduled maintenance to predictive maintenance, which optimizes maintenance schedules based on actual equipment health.
By leveraging IoT sensors, historical data, and AI, Azure ML enables organizations to forecast equipment failures before they occur, reducing downtime and maintenance costs.
Box 4: Extend with Microsoft 365 Copilot connectors
The finance department at your company wants AI-powered access to enterprise resource planning (ERP) data by using familiar productivity tools You can extend Microsoft 365 Copilot with connectors to enable AI-powered access to enterprise resource planning (ERP) data directly within productivity tools like Outlook, Teams, and Excel.
This integration allows users to query, summarize, and act on data from systems such as SAP, Oracle, and Dynamics 365 without switching between applications.
Reference:
https://techcommunity.microsoft.com/blog/azurecommunicationservicesblog/build-your-ai-email- agent-with-microsoft-copilot-studio/4448724
https://adoption.microsoft.com/en-us/scenario-library/human-resources/leave-of-absence- compliance-agent
https://learn.microsoft.com/en-us/azure/documentdb/solutions-iot
https://www.mtlc.co/how-to-unlock-the-power-of-microsoft-365-copilot-connectors
質問 # 46
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Microsoft Copilot provides a single AI app that has identical features and experiences across all Microsoft products. Answer: No
* Microsoft 365 Copilot delivers AI capabilities for business users that use Microsoft 365 apps.
The answer : Yes
* Microsoft Security Copilot helps companies understand risks and the organizational security posture.
The answer: Yes
* No - "Copilot" is an umbrella brand across Microsoft, but the experiences are not identical. Different Copilots target different workloads (productivity, security, development, business apps) and therefore expose different capabilities, connectors, permissions models, and admin controls. For example, Microsoft 365 Copilot is embedded in Word/Excel/PowerPoint/Outlook/Teams, while Security Copilot is built for SOC workflows and integrates with security tooling; they are intentionally not the same app with the same features.
* Yes - Microsoft 365 Copilot is specifically designed to deliver generative AI assistance for users working in Microsoft 365 applications. It enhances common business tasks such as drafting, summarizing, meeting recap, creating presentations, and working with documents and communications-directly inside the Microsoft 365 productivity suite.
* Yes - Microsoft Security Copilot is focused on security operations and helps analysts understand threats, investigate incidents, and improve visibility into security posture. Its purpose is aligned with helping organizations interpret security signals and risk context more efficiently, which supports understanding organizational risk and posture.
質問 # 47
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AB-731試験に合格しなかった、または難しすぎると認定試験を放棄したい場合は、Microsoft認定を取得した後にその利点について考えてください。 多くの特別なポジションでは、従業員に資格が必要です。 試験に合格することが非常に難しいと思われる場合は、AB-731有効な試験問題集PDFが目標の達成に役立ちます。 試験資料は実際のテストセンターから収集され、経験豊富な専門家によって編集されます。 100％の合格率が必要な場合、AB-731有効な試験対策PDFが役立ちます。
AB-731受験方法: https://www.jptestking.com/AB-731-exam.html