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Microsoft AI-102日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and Manage an Azure AI Solution | 15-20% | - Plan and configure secure AI solutions - Manage AI costs and resource allocation - Select appropriate Azure AI services and technologies - Implement governance and compliance for AI solutions |
| Topic 2: Implement Computer Vision Solutions | 15-20% | - Analyze images using Azure AI Vision - Implement object detection and image classification - Read and process documents using Azure AI Document Intelligence - Process video and generate insights - Implement Azure AI Face service |
| Topic 3: Implement Generative AI Solutions | 10-15% | - Apply responsible AI practices - Integrate Azure OpenAI models into applications - Use Azure OpenAI Service for generative AI - Implement prompt engineering techniques |
| Topic 4: Implement Natural Language Processing Solutions | 20-25% | - Build question answering solutions - Analyze text using Azure AI Language - Translate text and speech - Implement speech capabilities using Azure AI Speech - Create conversational AI solutions with Azure AI Bot Service |
| Topic 5: Implement Decision Support Solutions | 10-15% | - Monitor and optimize decision support solutions - Design and implement decision automation - Integrate Azure AI services into applications |
| Topic 6: Implement Knowledge Mining and Document Intelligence Solutions | 15-20% | - Design document processing pipelines - Create custom skills for Azure AI Search - Implement semantic and vector search - Implement Azure AI Search solutions |
AI-102日本語 Exam FAQ: Straight Answers
Microsoft recommends these training resources for the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) exam:
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The official Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) outline defines 6 domains. The leading three are Plan and Manage an Azure AI Solution (15-20%), Implement Knowledge Mining and Document Intelligence Solutions (15-20%), and Implement Generative AI Solutions (10-15%). The full list is in the topics section above, and TorrentExam's 425 practice questions cover each one.
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The AI-102日本語 exam is a Microsoft certification exam validating the Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) syllabus shown above. It belongs to these credential paths: Microsoft Certified: Azure AI Engineer Associate. Candidates in 2026 prepare for it with TorrentExam's 425 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
Recommended: Understanding of Azure services, REST APIs, C#, Python, or JavaScript. No mandatory prerequisites, but Azure Administrator or Developer background is beneficial. Since requirements change over time, confirm the latest on the official Microsoft exam page before scheduling.
The Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) exam requires 700 (on a scale of 1-1000) to pass, and registration costs $165 USD. A failed attempt means paying the same fee again — a readiness check with the TorrentExam engines before booking is the cheaper option.
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Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102日本語版) Sample Questions:
チャットボットを開発しています。
次のコンポーネントを作成します。
* QnA Maker リソース
* Azure Bot Framework SDK を使用したチャットボット
技術要件とチャットボットの要件を満たすために、追加のコンポーネントを追加する必要があります。何を追加すればよいでしょうか？
- A. Microsoft Translator
- B. チャットダウン
- C. ディスパッチ
- D. 言語理解
Correct Answer: C 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
Azure サブスクリプションをお持ちです。
ドキュメントの意味的類似性を比較するアプリを構築する必要があります。ソリューションは以下の要件を満たす必要があります。
* 各ドキュメントのトークンを表す数値ベクトルを返します。
* 開発の労力を最小限に抑えます。
どの Azure OpenAI モデルを使用すればよいですか?
- A. GPT-3.5
- B. Eから
- C. 埋め込み
- D. GPT-4
Correct Answer: C 🗳️
Explanation: Only visible for TorrentExam members. You can sign-up / login (it's free).
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
a tabular form of rows and columns
Rationale
Relational data is, by definition, data that adheres to the relational model .
In the relational model, data is organized into tables , which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form .
The other options are incorrect:
Unstructured data is the opposite of relational data.
A hierarchical folder structure is used for file organization, not the logical structure of relational data.
Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure , not the file format itself.
AH という名前の Azure OpenAI リソースと CS1 という名前の Azure Al Content Safety リソースを含む Azure サブスクリプションがあります。
All を使用して特定の質問に対する生成的な回答を提供し、CS1 を使用して入力と出力に不適切なコンテンツがないかチェックするチャットボットを構築します。
サンプルの質問に対してテストを実行して、コンテンツ フィルターの構成を最適化する必要があります。
解決策: Content Safety Studio から、保護された素材の検出機能を使用してテストを実行します。
これは要件を満たしていますか?
- A. はい
- B. いいえ
Correct Answer: B 🗳️
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Azure Al Foundry エージェント サービスを使用してエージェントを構築しています。
エージェントが過去 90 日間にリリースされた公開データにアクセスできることを確認する必要があります。
コードをどのように完成させるべきですか? 回答するには、回答エリアで適切なオプションを選択してください。
注意: 正しい選択ごとに 1 ポイントが付与されます。
Correct Answer:
Explanation:
To let an agent use public web data with recency controls (e.g., limit to content from the last 90 days), you add the Bing grounding tool. In the Azure AI Foundry Agent Service SDK, you create a connection (e.g., search_connection_id ) and then construct a BingGroundingTool with that connection. The tool exposes a list of definitions (tool operations) that the agent can call at runtime.
When creating the agent, you pass tool definitions via the tools= argument (a list of ToolDefinition ). The tool_resources= argument is used when the tool requires additional resource descriptors (e.g., Azure AI Search index configuration). For Bing grounding, providing the connection and passing the definitions as tools= is sufficient. This enables the agent to call Bing with date filters to retrieve only recent, publicly accessible content.
Microsoft Azure AI References (titles only)
Azure AI Agent Service - Bing web grounding tool
Azure AI Agent Service - Create an agent (agent creation parameters including tools and tool_resources ) Azure AI Agent Service - Tool connections and definitions
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