Microsoft AI-103-JPN Questions & Answers - in .pdf
- Total Q&A: 152
- Update: Oct 28, 2026
- Price: $69.99
- Vendor: Microsoft
- Exam Code: AI-103-JPN
- Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
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Microsoft AI-103-JPN Q&A - Testing Engine
- Total Q&A: 152
- Update: Oct 28, 2026
- Price: $69.99
- Vendor: Microsoft
- Exam Code: AI-103-JPN
- Exam Name: Developing AI Apps and Agents on Azure (AI-103日本語版)
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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Objectives |
|---|
| Topic 1: Implement Natural Language Processing Solutions | - Translation and multilingual support
- Text analytics and summarization
- Language understanding and intent recognition
|
| Topic 2: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
- 1. Function calling and tool use
- 2. Prompt engineering and prompt flow design
- AI agents architecture
- 1. Memory and state management
- 2. Agent orchestration and workflows
|
| Topic 3: Knowledge Mining and Information Retrieval | - Azure AI Search configuration
- RAG (Retrieval Augmented Generation) patterns
- Indexing and semantic search
|
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection
- OCR and document intelligence
|
| Topic 5: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration
- Responsible AI principles and governance
- Model selection and lifecycle management
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
Question #1
エージェントを含む Microsoft Foundry プロジェクトがあります。
スキャンされたテキスト、表、複数列レイアウトを含む、様々な形式のドキュメントを処理する必要があります。抽出されたコンテンツはドキュメントの構造を保持し、後続の処理のためにMarkdown形式に変換する必要があります。
最初に何を設定すべきですか？
A. マルチモーダルモデルを使用するAzure OpenAl Responses API呼び出し
B. 生成型チャット完了リクエスト
C. Foundry Tools のテキスト分析モデル展開における Azure 言語
D. Foundry Tools の Azure コンテンツ理解アナライザー
Question #2
Microsoft Foundry プロジェクトがあります。リアルタイム推論をサポートするために、モデルカタログからモデルをデプロイする必要があります。ソリューションは次の要件を満たす必要があります。
* キーベース認証を使用する
* リアルタイムREST APIアクセスをサポート
* Azure サブスクリプション内の仮想マシンの vCPU クォータを消費しない どのタイプのデプロイメントを使用すべきですか?
A. サーバーレスAPI
B. 標準
C. セルフホスト型コンテナ
D. バッチ
Question #3
画像が10万枚あります。
以下の動作を実行するアプリを作成する必要があります。
* 画像内の道路標識を識別し、標識に書かれているテキストを抽出します。
* テキストを分析して、よく知られた場所を特定してください。
解決策は開発労力を最小限に抑えるものでなければならない。
Question #4
顧客サポートアプリケーションを含むMicrosoft Foundryプロジェクトがあります。
Run1とRun2という名前の2つの評価を作成し、それぞれに以下の設定を行います。
* 両方ともリスクと安全性の指標を含んでいます
* 両方とも保護対象物の評価を含みます
* Run1には、中程度の70の閾値を使用する有害コンテンツ指標が含まれています
* Run2には、高い深刻度しきい値を使用する有害コンテンツ指標が含まれています。D61という名前のデータセットに対して両方の評価を実行すると、次の結果が得られます。
* Run1のコンテンツ損傷率：12%
* コンテンツに損傷を与える欠陥がRun2.4%遅れて発生
* 保護対象材料の評価 o ' 実行1: 6%
* Run2の保護対象物質評価：6%
081を使用して微調整ジョブを開始します。このジョブは、複数のコンテンツ有害性タイプに対する自動RAIチェック中に失敗します。
コンテンツフィルタリングの設定が「高」の深刻度に設定されていることがわかった。
以下の各記述について、正しい場合は「はい」を選択してください。そうでない場合は「いいえ」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。
Question #5
エージェントとGitHubリポジトリを含むMicrosoft Foundryプロジェクトがあります。リポジトリには、評価設定を定義するYAMLファイルが含まれています。
プルリクエストが開かれたときに評価を実行するGitHub Actionsワークフローが必要です。
A. evaluation-config を YAML ファイルのパスに設定します。
B. tenant-id を Microsoft Entra テナント ID に設定します。
C. project-endpoint をプロジェクトのエンドポイントに設定します。
D. model-ployment-name をデプロイされたモデルに設定します。
Solutions:
Question #1
Correct Answer: D | Question #2
Correct Answer: A | Question #3
Correct Answer: Only visible for members | Question #4
Correct Answer: Only visible for members | Question #5
Correct Answer: C |
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