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Microsoft AI-103日本語 Exam Syllabus Topics:

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

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

1. 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、取得したポリシー文書から概要を生成します。
応答の完全性を改善する必要があります。この解決策は、応答が返される前に、アプリケーションコードのロジックに実装されなければなりません。
あなたはどうすべきでしょうか?

A) max_tokens パラメータの値を減らします。
B) レスポンスを返す前に再試行評価を追加します。
C) より小規模な展開でモデルを置き換えます。
D) Retrieval Augmented Generation (RAG) に切り替えます。


2. 副操縦士は、会話の以前のやり取りに依存する複数の質問に答える必要があります。システムが複雑な質問をそれぞれ焦点を絞ったサブクエリに分割し、それらを並列実行し、回答を作成する前に結果を意味的に再ランク付けするようにしたい場合、Azure AI Search のどの機能がこれを提供しますか?

A) 高い類似度閾値を持つ単一ベクトルクエリ
B) エージェントによる検索
C) 単一キーワードクエリにおけるトップk値の増加
D) スコアリングプロファイルを使用したクラシックなキーワード検索


3. 開発者は、推論を実行してエージェントを使用するために、Microsoft Foundry プロジェクトのエンドポイントを呼び出すためのマネージド ID を必要とします。セキュリティ ポリシーでは、データ プレーンへのアクセスのみを許可する最小限の権限を持つロールが求められています。マネージド ID には、どの組み込みロールを割り当てるべきですか?

A) Foundryアカウント所有者
B) 認知サービス貢献者
C) Azure AI開発者
D) 鋳造所ユーザー


4. Azure AI Studioでは、GPT-35 Turboモデルを使用してCompletionsプレイグラウンドを利用します。
プロンプトに以下のコードが含まれています。
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
コードの説明を作成するにはモデルが必要です。解決策はコストを最小限に抑えるものでなければなりません。
あなたはどうすべきでしょうか?

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


5. あなたはMicrosoft Foundryプロジェクトを持っています。
エージェントを含む顧客サポートソリューションを構築する予定です。このソリューションは、以下の要件を満たす必要があります。
- 内部情報に基づいた、正確で状況に応じた回答を提供する
製品ドキュメントはAzure AI Searchに保存されています。
・長文の文脈にわたる、深く多段階的な推論を必要とする。
- 詳細な自然言語による応答を生成します。
エージェントを動かすために、どのタイプのモデルを使用すべきでしょうか?

A) 小型言語モデル(SLM)
B) マルチモーダルモデル
C) 大規模言語モデル(LLM)
D) キーワード抽出モデル


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: C

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