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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
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。
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.
Azure Speech アプリケーションが Microsoft Entra ID トークンを使用してサービスに対して認証できることを確認する必要があります。
どの2つの行動をとるべきですか？
- A. プライベートエンドポイントを作成します。
- B. 仮想ネットワークサービスエンドポイントを有効にします。
- C. 条件付きアクセス ポリシーを作成します。
- D. X.509証明書を要求します。
- E. カスタムサブドメインを設定します。
Correct Answer: A,E 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントの知識源は、Azure Blob Storageに保存されているスキャン済みのPDFトラブルシューティングガイドのセットです。ガイドページは2列レイアウトと表で構成されています。
PDFを処理するには、Foundry ToolsのAzure Content Understandingを使用します。
処理済みのコンテンツをインデックスに取り込み、検索拡張生成(RAG)に利用し、抽出したフィールドを後続の自動化のために保存する予定です。
関係者は、抽出された各フィールド値が元のPDFのどこから来たのかを確認できなければならず、信頼性の低い抽出結果は手動レビューのために回送する必要がある。
コンテンツ理解ドキュメントアナライザーの出力に、フィールドごとの信頼度スコアと、ソースドキュメント内のソース特定箇所が含まれていることを確認する必要があります。
あなたはどうすべきでしょうか？
- A. アナライザーを、すべてのフィールドに対して生成抽出を使用するように設定します。
- B. enableSegment を true に設定します。
- C. estimateFieldSourceAndConfidence を有効にします。
- D. ラベル付きサンプルを提供する。
Correct Answer: C 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
Microsoft Foundryプロジェクトがあり、その中にFoundry Agent Serviceを使用して構築されたカスタマーサポートエージェントが含まれています。
エージェントは、チケット発行ツールを通じてユーザーから提供されたスクリーンショットをAzureストレージにアップロードし、追加の分析に必要なBLOB URLを受け取ります。
エージェントの実行中は画像モデレーションを使用し、実行中に有害なコンテンツが返されないようにする必要があります。Azure AI Content Safety は、BLOB URL を使用して画像にアクセスする必要があります。このソリューションは、最小権限の原則に従う必要があります。
コンテンツの安全性に関して、どのような設定を行うべきですか？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:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block
. Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
あなたは、Foundry Tools API の Azure Vision を使用して画像を分析するアプリを開発しています。
アプリが画像が説明文なのか線画なのかを識別するために使用するリクエストを設定する必要があります。
リクエストはどのように完了すればよいですか？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:
Use an HTTP POST request to the Analyze Image endpoint and request the imageType visual feature.
The image-analysis operation receives image content or an image reference and evaluates the visual features specified in the request. imageType is the feature that supplies information indicating whether an image is clip art or a line drawing.
This is distinct from other feature values shown in the exhibit. description produces a textual image description. Objects performs object detection and returns detected objects and bounding boxes. tags produces semantic labels describing visual concepts. None of those provides the specific clip-art/line-drawing classification requested here.
The pretrained image-type capability eliminates the need to create and train a Custom Vision classifier to distinguish ordinary photographs from drawings. The application can simply examine the returned image-type properties and implement its classification policy.
The use of POST is consistent with submitting image data to the image-analysis endpoint.
The AI-103 Study Guide includes visual understanding workflows and selecting appropriate Foundry
/Vision capabilities for identifying visual characteristics. Microsoft Learn
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