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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 5: 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:
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。
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
あなたはMicrosoft Foundryプロジェクトを持っています。
リアルタイム推論をサポートするには、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。ソリューションは以下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。
- リアルタイムのREST APIアクセスをサポートします。
- Azure の仮想マシンの vCPU クォータを消費しない
サブスクリプション。
どのタイプのデプロイメントを使用すべきでしょうか？
- A. サーバーレスAPI
- B. バッチ
- C. 標準
- D. セルフホスト型コンテナ
Correct Answer: C 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
ホットスポットに関する質問
あなたは、検索拡張生成(RAG)ソリューションを含むMicrosoft Founcyプロジェクトを持っています。
クエリ、コンテキスト、レスポンス、正解データを含むラベル付きCSVデータセットを使用して、本番環境前の評価を実行する必要があります。評価では、以下の項目を測定する必要があります。
- 回答がユーザーの質問に対応しているかどうか
- 回答が提供された文脈によって裏付けられているかどうか
回答に機密情報または専有情報が含まれているかどうか
どのAI品質評価指標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。
Correct Answer:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
あなたは、短い製品プロモーションビデオを生成するMicrosoft Foundryプロジェクトを持っています。
いくつかの動画が承認された後、審査員は一部の動画の右上隅に小さな透かしが入っていることに気づく。
動画を再生成せずにウォーターマークを削除する必要があります。
あなたはどうすべきでしょうか？
- A. 動画の該当部分にマスクベースのインペインティング編集を適用します。
- B. ガイダンススケールを拡大します。
- C. 元のプロンプトを修正して透かしを除外します。
- D. サイズパラメータを使用してビデオをトリミングします。
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
Azure OpenAIリソースを含むAzureサブスクリプションをお持ちです。
与えられた商品説明から商品名を提案するアプリを開発する必要があります。
どのモデルを使用すべきですか？
- A. ささやき
- B. GPT-4
- C. 埋め込み
- D. DALL-E
Correct Answer: B 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
