Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
Certification Provider: Microsoft
Corresponding Certification: Azure AI Engineer Associate
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
エージェントを含む Microsoft Foundry プロジェクトがあります。このエージェントは、取得ツールとして Azure AI Search を使用します。
エージェントがドキュメントと埋め込み画像の両方のテキストに基づいて応答を生成できるように、PDFをAzure AI Searchインデックスに取り込むことを計画しています。
利用者は、出典ファイルへのリンクを含む引用を求めている。
インデックス作成の際には、画像が内蔵の光学文字認識(OCR)機能の入力として使用できる構造に抽出されるようにする必要があります。
どのインデックス作成方法を採用すべきでしょうか？
- A. OCR入力を再構築するシェイパースキル
- B. インデックスのコンテンツフィールドに対してOCRスキルを直接実行するスキルセット
- C. 画像データを正規化された画像コレクションに抽出するインデクサー
- D. 画像データを検索可能なフィールドに書き込むためのoutputFieldMappingsパラメータ
Correct Answer: C 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
Foundry Tools の Azure Content Understanding を使用する App1 という名前の請求書処理アプリケーションがあります。Pipeline1 という名前の新しい Content Understanding パイプラインを構築しますが、このパイプラインは、次の要件を満たす必要があります。
請求書とそれに関連する発注書を比較する。
* 請求書を静的な仕入先契約書と照合して検証する。
* 不一致箇所を含む、単一の構造化された出力を返します。
Pipeline1を設定し、パイプラインを単一のアナライザーエンドポイントとして公開する必要があります。何を設定すればよいでしょうか？
- A. 抽出フィールドに対して信頼度スコアが有効になっている標準モードの単一ファイルタスク
- B. 標準モードで提供されるベンダー契約書を分析時に追加文書として使用する単一ファイルタスク
- C. ベンダー契約ファイルを参考データとして使用するプロモードの複数ファイルタスク
- D. 請求書と発注書をアナライザーへの入力として使用する、標準モードの複数ファイルタスク
Correct Answer: C 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントが複数の会話にわたってユーザー設定を記憶できるようにするには、長期記憶を有効にする必要があります。保存される記憶は、クライアントアプリケーションがユーザーIDを手動で生成することなく、認証済みユーザーごとに分離されている必要があります。
Pythonコードを完成させるにはどうすればよいでしょうか？適切な値を正しい場所にドラッグして回答してください。
各値は、1回だけ使用することも、複数回使用することも、全く使用しないこともできます。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
scope = " {{userId}} "
tools = [memory_tool]
The correct scope value is {{userId}} because the requirement is per-authenticated-user memory isolation without the client application manually generating user identifiers. In Microsoft Foundry Agent Service memory, the scope parameter partitions memory items inside the memory store. The official guidance states that when the memory search tool is attached to an agent, setting scope to the user identity template enables per-user memory isolation; the service resolves the end-user identity from the request header when provided, or falls back to the Microsoft Entra tenant ID and object ID of the caller. This matches the requirement to isolate stored preferences by authenticated user automatically.
The tools property must be [memory_tool] because the MemorySearchTool instance is created earlier and must be attached to the PromptAgentDefinition. Foundry guidance shows the memory search tool being passed in the agent definition as tools=[tool] , allowing the agent to read from and write to the configured memory store during conversations.
" session " and {{conversationId}} would limit continuity to a session or conversation instead of enabling long-term recall across separate conversations. [mem_store_name] is a list containing the store name, not a tool definition. Reference topics: Foundry Agent Service memory, memory stores, memory search tools, scope, and per-user isolation.
顧客サポートエージェントを含む Microsoft Foundry プロジェクトがあるとします。このエージェントは、応答を生成する前に、内部のナレッジ API ツールを呼び出します。
ユーザーから以下の問題が報告されています。
※一部のリクエストは完了までに15秒以上かかる場合があります。
* 知識APIが期待されるデータを返した場合でも、一部の応答が誤っている場合があります。
大規模言語モデル(LLM)呼び出し、ツール呼び出し、およびタイミング情報の順序を確認するには、個々のエージェントの実行を検査する必要があります。
どの監視機能を使用すべきでしょうか？
- A. 安全指標
- B. トークンの使用
- C. モニタリング
- D. トレース
Correct Answer: D 🗳️
Explanation: Only visible for ExamsLabs members. You can sign-up / login (it's free).
感情分析と光学文字認識(OCR)を実行するために使用する新しいリソースを作成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。
単一のキーとエンドポイントを使用して、複数のサービスにアクセスできます。
今後利用する可能性のあるサービスの請求を一本化します。
* 将来的にFoundry ToolsでAzure Visionの使用をサポートする。
新しいリソースを作成するためのHTTPリクエストはどのように完了すればよいですか？回答するには、回答欄で適切なオプションを選択してください。
注：正解ごとに1ポイントが加算されます。
Correct Answer:
Explanation:
Azure Resource Manager uses an HTTP PUT request to create or update a resource at a specified resource ID. Therefore, the first dropdown must be PUT.
The resource must use the CognitiveServices kind because the requirements describe a multi-service Azure AI resource rather than an individual service-specific resource.
A TextAnalytics-specific kind would provide Language functionality but would not satisfy the requirement for a single resource covering OCR and future Vision use. ComputerVision would similarly focus on Vision rather than consolidating multiple Azure AI service capabilities.
The multi-service resource allows applications to access supported Azure AI services while centralizing resource administration and billing. A single Azure AI services resource can therefore serve as the account boundary for capabilities such as Language sentiment analysis and Vision OCR.
The request should target the Microsoft.CognitiveServices/accounts resource type and supply the selected kind and SKU in the body. Identity configuration may additionally be applied depending on the security architecture.
The AI-103 Study Guide requires candidates to design Azure infrastructure for AI apps and choose appropriate Foundry services as part of planning and managing an Azure AI solution. Microsoft Learn
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