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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
Azure OpenAt の構造化出力を使用して、非構造化レシートテキストとサポートチケットテキストからフィールドを抽出する Python アプリケーションがあります。このアプリケーションは、次のスキーマを使用します。
Correct Answer:
Explanation:
* The schema requires that a receipt has a discount_code value - No
* The schema can be used as is for Azure OpenAI structured outputs - No
* The generated JSON object will preserve the property order merchant, order_number, discount_code, line_items, and total - Yes The discount_code definition permits a nullable result. Consequently, the output can contain " discount_code
" : null when the source receipt contains no discount code. The property can be structurally present without containing a substantive discount-code value; therefore, the first statement is No .
The schema cannot be submitted unchanged. Azure OpenAI structured outputs support only a defined subset of JSON Schema. Every property declared in an object must be included in that object's required array.
Optional information must instead be represented as a required property whose type includes null. Each object must also specify " additionalProperties " : false. Furthermore, although nested anyOf constructs are supported, an anyOf construct cannot be used as the root schema. A schema that violates any of these restrictions must be revised before use, making the second statement No .
The third statement is Yes . Azure OpenAI structured outputs preserve keys in the order in which the properties are defined in the supplied schema. Therefore, the receipt object will emit merchant, order_number, discount_code, line_items, and total in that sequence. This behavior supports predictable parsing and downstream integration. The relevant curriculum area is Use Azure OpenAI in Foundry Models to generate content , including application integration and structured model responses.
Microsoft 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あります。このエージェントは、Azure OpenAL モデルのデプロイメントを呼び出します。
負荷テスト中に、呼び出しが断続的に失敗し、HTTP 429 レート制限超過エラーが返されます。
負荷がかかった状態での通話失敗を減らし、信頼性を向上させるためには、スロットリングを適切に処理する必要があります。このソリューションは、サービスおよびモデルの制限内に収ま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. アップロードされたコンテンツをより小さなファイルに分割します。
- B. 登録ツールの数を減らします。
- C. 新しいスレッドを作成し、すぐに呼び出しを再試行します。
- D. 指数バックオフとジッターを使用する再試行ポリシーを実装します。
Correct Answer: D 🗳️
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Project1という名前のMicrosoft Foundryプロジェクトがあり、その中にエージェントが含まれています。このエージェントは、OpenAPI 3.0仕様を使用して外部の気象サービスを呼び出します。
天気予報サービスを利用するには、HTTPヘッダーにキーを渡す必要があります。キーの値は、Project1の接続情報として保存されます。
OpenAPIツールが呼び出されるたびに、接続からのキー値が自動的に含まれるようにする必要があります。
OpenAPI仕様では何を設定すべきですか？
- A. APIキーセキュリティスキーム
- B. Azure Key Vaultへの接続
- C. ベアラートークンセキュリティスキーム
- D. 各操作ごとに定義されたヘッダーパラメータ
Correct Answer: A 🗳️
Explanation: Only visible for DumpsFree members. You can sign-up / login (it's free).
Azure サブスクリプションに、Resources という名前の Azure Language in Foundry Tools リソースが含まれています。次の cURL コマンドを実行してから、Outputmp3 ファイルを再生します。
以下の各設問について、該当する場合は「はい」を選択してください。該当しない場合は「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。
Correct Answer:
Explanation:
The SSML uses three distinct neural voices: an en-US Jenny voice, an en-GB Ryan voice, and an en-US Christopher voice. Therefore, the output contains sentences produced by three different voices , making the first statement Yes .
The second statement is No . There are three voices but not three unique locale/accent configurations. Two voices use en-US while one uses en-GB. Consequently, the audio does not represent three different accents
/locales.
The third statement is also No . The Christopher voice is wrapped inside an mstts:express-as element with an expressive style such as advertisement_upbeat, with a configured style degree. That instruction intentionally alters speaking style and therefore is not a neutral presentation.
SSML permits a single synthesis request to contain multiple < voice > elements, enabling multiple voices to appear in one output audio stream. SSML also supports style controls, pronunciation controls, pauses, pitch, speed, and other speech characteristics.
The AI-103 Study Guide includes text-to-speech for agentic interactions and speech modality configuration within text-analysis solutions. Microsoft Learn
Azure AI Custom Visionと独自にトレーニングした分類器を使用して画像内の製品を識別するアプリがあります。分類器に新しい製品を追加する必要があります。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。
* 商品を追加するのにかかる時間を最小限に抑える。
開発労力を最小限に抑える。
どの5つの行動を順番に実行すべきでしょうか？回答するには、行動リストから適切な行動を回答欄に移動させ、正しい順序に並べ替えてください。
Correct Answer:
Explanation:
Because the existing classifier is an Azure AI Custom Vision project, the lowest-effort workflow is performed directly in the Custom Vision portal , not Azure Machine Learning studio or Vision Studio. Open the existing project so that the new product classes can be incorporated into its current training dataset.
Next, upload representative sample images of each new product. The images should include sufficient variation in viewing angle, lighting, background, size, and presentation to support reliable classification.
Label each image with the appropriate new product tag. Custom Vision uses these tags as the target classes during supervised training. Microsoft's portal workflow explicitly supports uploading and manually tagging images without requiring application code.
Retrain the classifier after the labeled images have been added. Training creates a new iteration based on all current training images and tags, including the newly introduced product categories.
Finally, publish the trained iteration to the prediction resource. Publishing makes that iteration available through the Custom Vision Prediction API used by the application.
Study Guide alignment: create, train, evaluate, retrain, publish, and consume custom image-classification models .
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