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Microsoft AI-103日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement generative AI solutions25-30%- Optimize and evaluate models
  • 1. Configure content filters and safety
  • 2. Evaluate responses and grounding
  • 3. Implement multimodal AI capabilities
- Develop generative AI applications
  • 1. Implement prompt engineering
  • 2. Use Azure OpenAI and Foundry models
  • 3. Build retrieval-augmented generation solutions
Topic 2: Plan and manage Azure AI solutions25-30%- Plan Azure AI resources
  • 1. Select Azure AI services and Foundry resources
  • 2. Manage deployments and monitoring
  • 3. Configure authentication and security
- Manage AI solution lifecycle
  • 1. Apply responsible AI practices
  • 2. Implement CI/CD for AI applications
  • 3. Monitor model and application performance
Topic 3: Implement text analysis and information extraction solutions10-15%- Analyze and extract information
  • 1. Implement natural language processing
  • 2. Extract entities and structured data
  • 3. Use document intelligence services
Topic 4: Implement computer vision solutions10-15%- Analyze visual content
  • 1. Use multimodal vision APIs
  • 2. Implement OCR and visual understanding
  • 3. Process images and video
Topic 5: Implement agentic solutions20-25%- Build AI agents
  • 1. Configure memory and orchestration
  • 2. Create autonomous and multi-agent workflows
  • 3. Integrate tools and external knowledge
- Manage agent operations
  • 1. Monitor and debug agents
  • 2. Secure agent interactions
  • 3. Implement scalable deployments

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

Question #1

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. マルチモーダルモデル
  • B. 小型言語モデル(SLM)
  • C. キーワード抽出モデル
  • D. 大規模言語モデル(LLM)
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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Question #2

デプロイ済みのチャットモデルに基づいて構築され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。
エージェントの応答は、生成された回答を保存済みの期待される出力と比較する自動テストシステムを使用して検証されます。自動テストの失敗を防ぐため、同一のプロンプトに対しては一貫した応答が返される必要があります。
質問内容を変更したり、事実の正確性を損なったりすることなく、回答のばらつきを減らす必要があります。
モデルに対して何をすべきでしょうか?

  • A. max_tokens パラメータを増やします。
  • B. 温度パラメータを下げます。
  • C. 温度パラメータを上げます。
  • D. リクエストから停止シーケンスを削除します。
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

ホットスポットに関する質問
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という名前の評価を作成し、以下の構成を設定します。
リスクと安全性の指標を含む
- 保護対象物の評価を含む
- 中程度の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ます。以下の構成を持つ Run2 という名前の評価を作成します。
リスクと安全性の指標を含む
- 保護対象物の評価を含む
- 高い深刻度閾値を使用する有害コンテンツ指標を含む
DB1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。
- 実行1におけるコンテンツ損傷率:12%
- Run2のコンテンツ損傷率:4%
- Run1の保護対象物質評価:6%
- Run1の保護対象物質評価:6%
DB1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。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Box 1: No
Changing the content filtering configuration to low severity will not resolve the fine-tuning job issues.
Box 2: Yes
The difference between a 12% defect rate in Run1 and a 4% defect rate in Run2 is entirely consistent with the different severity thresholds configured for the two evaluations.
Box 3: Yes
The protected material evaluation metric is entirely unaffected by changes to the content harm severity threshold.
Reference:
https://learn.microsoft.com/en-us/python/api/azure-ai-evaluation/azure.ai.evaluation

Question #4

ホットスポットに関する質問
Azure AI Search を使用するエンリッチメント パイプラインを作成しています。ナレッジ ストアには、構造化されていない JSON データと、スキャンされた PDF ドキュメントのテキストが含まれています。
各データタイプには、どの投影法を使用すべきでしょうか?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。

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
Box 1: Object projection
For the unstructured JSON data, you must use object projection, and for the extracted text data from the scanned PDF documents, you must use file projection.
Object Projection: Projects your data as a full JSON representation. It is ideal for maintaining the original structure alongside any applied AI enrichments within a single JSON document inside Azure Blob Storage.
Box 2: File projection
File Projection: Captures the binary or image extraction layer required during Optical Character Recognition (OCR) processing. It isolates and outputs raw text and graphic details directly from unstructured physical binary files into a designated container Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-concept-image-scenarios

Question #5

お客様は、AI1 という名前の Azure AI Foundry インスタンスを含む Azure サブスクリプションをお持ちです。
システムログファイルに記録された問題を自動的にトリアージして解決するアプリをお持ちですね。
問題解決のために連携するインシデントマネージャーエージェントとDevOpsエージェントを作成します。
インシデントマネージャーエージェントがDevOpsエージェントに作業を割り当てられるようにする必要があります。ソリューションは開発作業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. AI1 用の接続エージェントを設定します。
  • B. AI1のプロンプトフローを設定します。
  • C. DevOpsエージェント用のプラグインを作成します。
  • D. インシデントマネージャーエージェント用のプラグインを作成します。
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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