Passing exam with AI-103日本語 latest training materials

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Updated: Nov 12, 2026

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Microsoft AI-103日本語 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing AI Apps and Agents on Azure
Exam Number:AI-103
Real Exam Qty:50–60
Certificate Validity Period:1 year (renewable annually via free online assessment)
Related Certifications:Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026)
Exam Price:$165 USD (varies by country/region)
Exam Format:Scenario-based, Case study, Performance-based items, Multiple choice
Passing Score:700 / 1000
Exam Duration:100 minutes
Available Languages:Spanish, Japanese, German, French, Korean, Chinese (Simplified), English, Portuguese (Brazil)
Recommended Training:Azure AI Foundry Documentation
Microsoft Learn: Develop AI Apps and Agents on Azure
Exam Registration:Pearson VUE Registration
Microsoft Certification Dashboard
Sample Questions:Microsoft AI-103日本語 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103

Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Plan and manage Azure AI solutions25–30%- Manage AI solution development lifecycle
  • 1. Configure model and agent deployments
  • 2. Integrate with CI/CD pipelines
  • 3. Monitor and maintain AI workloads
- Design Azure AI infrastructure
  • 1. Design for scalability, availability, and cost optimization
  • 2. Select appropriate Azure AI Foundry services
  • 3. Plan for security, compliance, and responsible AI
Topic 2: Implement generative AI and agentic solutions30–35%- Build generative AI applications
  • 1. Implement prompt engineering and optimization
  • 2. Integrate Azure OpenAI and other models
  • 3. Build retrieval-augmented generation (RAG) solutions
  • 4. Implement function calling and tool use
- Design and implement intelligent agents
  • 1. Select agent architecture patterns
  • 2. Implement multi-agent workflows and orchestration
  • 3. Integrate agents with external systems and data sources
  • 4. Manage state, memory, and context
Topic 3: Implement text and speech analysis solutions10–15%- Implement natural language processing
  • 1. Perform sentiment analysis, entity recognition, and summarization
  • 2. Use Azure AI Language services
  • 3. Build conversational language understanding
- Implement speech capabilities
  • 1. Speech-to-text and text-to-speech integration
  • 2. Speech translation and speaker recognition
Topic 4: Implement information extraction and knowledge mining10–15%- Build knowledge bases and search solutions
  • 1. Design knowledge mining pipelines
  • 2. Create and manage vector indexes
  • 3. Implement Azure AI Search
- Extract structured data from documents
  • 1. Use Azure AI Document Intelligence
  • 2. Process forms, invoices, and unstructured content
Topic 5: Implement computer vision solutions10–15%- Implement image analysis and processing
  • 1. Use Azure AI Vision services
  • 2. Implement object detection and image classification
  • 3. Extract text and structure from images
- Build multimodal solutions
  • 1. Combine vision and language capabilities
  • 2. Process and analyze video content

Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:

Question #1

社内ドキュメントに基づいたカスタマーサポートエージェントを含むMicrosoft Foundryプロジェクトがあります。
最近のアップデート後、ユーザーから以下の問題が報告されています。
* 一部の回答は、取得された文書によって裏付けられていません。
* ごく一部の回答がポリシー違反としてフラグ付けされています。
それぞれの問題を評価する必要があります。
各課題に対して、どの可観測性シグナルを使用すべきでしょうか?回答するには、適切な可観測性シグナルを正しい課題にドラッグしてください。各可観測性シグナルは、1回、複数回、または全く使用しない場合があります。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要がある場合があります。
注:正解ごとに1ポイントが加算されます。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Unsupported responses: Groundedness evaluation metrics
Policy violations: Risk and safety metrics
For unsupported responses, use Groundedness evaluation metrics . In a Retrieval Augmented Generation scenario, the key question is whether the generated answer is supported by the retrieved context. Microsoft Foundry built-in evaluators define Groundedness as the RAG metric that measures how grounded a response is in retrieved context and returns a model-based score; Groundedness Pro evaluates whether the response is grounded in retrieved context by using Azure AI Content Safety. This directly matches answers that are unsupported by internal documentation.
For policy violations, use Risk and safety metrics . Microsoft Foundry risk and safety evaluators assess generated responses for safety risks such as hate and unfairness, sexual content, violence, self-harm, protected material, indirect attacks, code vulnerability, ungrounded attributes, prohibited actions, and sensitive data leakage. The guidance states that these evaluators assign risk and safety severity or pass/fail outcomes for AI responses and agent behavior.
Latency breakdown traces diagnose performance, not correctness or policy compliance. Token usage analytics diagnose cost and prompt/response size, not unsupported claims or safety violations. Reference topics:
Microsoft Foundry observability, RAG evaluators, groundedness, risk and safety evaluators, and agent quality evaluation.

Question #2

リアルタイムGPTモデルには、クライアント側でのライブオーディオストリーミングと約100ミリ秒のレイテンシをサポートする接続方法が必要です。

  • A. WebSocket
  • B. SIP
  • C. RTMP
  • D. WebRTC
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #3

モデルをトレーニングすることなく、各メールから人物、組織、場所、日時などの事前定義されたエンティティカテゴリを抽出する必要があります。

  • A. recognize_entities を呼び出します。
  • B. スキルセットで NamedEntityRecognitionSkill を設定します。
  • C. recognize_pii_entities を呼び出します。
  • D. カスタムエンティティ認識を使用する分析ジョブを送信します。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

ユーザーのビデオストリームを監視し、ユーザーが一人でいることを確認する必要があります。このソリューションは、開発の手間を最小限に抑える必要があります。

  • A. カスタムビジョンにおける物体検出
  • B. Azure Visionにおける空間分析
  • C. 音声認識
  • D. Azure AI Video Indexer におけるコンテンツモデレーション
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

Microsoft Foundry では、GPT-35 Turbo モデルを使用してチャット プレイグラウンドを使用します。すると、次のコードを含むプロンプトが表示されます。

コードの説明を作成するためにモデルが必要です。解決策はコストを最小限に抑える必要があります。どうすればよいですか?

  • A. プロンプトに // 関数 F は何をしますか? を追加します。
  • B. プロンプトに機能 F (説明) を追加します。
  • C. モデルを GPT-4-32k に変更します。
  • D. 温度パラメータを1に設定します。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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