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Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
Azure サブスクリプションに、Foundry Tools の Azure Language サービス リソースが含まれています。この Language サービスの REST インターフェイスの URL を特定する必要があります。Azure ポータルでどのブレードを使用すればよいですか？
- A. アイデンティティ
- B. 特性
- C. ネットワーキング
- D. キーとエンドポイント
Correct Answer: D 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
内部ポリシー文書のテキストとユーザークエリのベクトル表現を生成するモデルが必要です。
- A. 埋め込みモデル
- B. 大規模言語モデル
- C. 小型言語モデル
- D. 画像生成モデル
Correct Answer: A 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
Microsoft Foundry では、GPT-35 Turbo モデルを使用してチャット プレイグラウンドを使用します。すると、次のコードを含むプロンプトが表示されます。
コードの説明を作成するためにモデルが必要です。解決策はコストを最小限に抑える必要があります。どうすればよいですか？
- A. プロンプトに機能 F (説明) を追加します。
- B. モデルを GPT-4-32k に変更します。
- C. プロンプトに // 関数 F は何をしますか? を追加します。
- D. 温度パラメータを1に設定します。
Correct Answer: C 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
オブジェクト検出を実行する Azure Custom Vision サービス プロジェクトがあります。このプロジェクトは分類に General ドメインを使用し、学習済みのモデルを含んでいます。
インターネットに接続されていないネットワークで使用するために、モデルをエクスポートする必要があります。
どの3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:
Custom Vision model export is supported for compact domains . The existing project uses the ordinary General domain, so the first action is to change the domain to General (compact) .
A compact domain uses a model architecture specifically designed to support local/edge inference and export.
Changing the domain alone does not transform the already-trained iteration into a compact model. Therefore, the second action is to retrain the model so that a new iteration is generated using the compact-domain architecture.
After the compact iteration has been trained, export the model in an appropriate supported format. The resulting model package can then be deployed within the disconnected environment and perform inference without contacting the Azure-hosted Custom Vision prediction service.
Creating a new classification model is incorrect because the existing scenario is object detection. Changing the classification type is also unnecessary and would not address exportability.
The required sequence is therefore:
General # General (compact) # retrain # export
This is a standard Custom Vision edge-deployment workflow and directly supports environments with restricted or unavailable internet connectivity.
Study Guide alignment: implement solutions that identify objects/components and deploy computer- vision functionality appropriate to the target environment .
あなたは、Foundry Tools の Azure Content Understanding を使用してマーケティング動画を分析する Microsoft Foundry プロジェクトを持っています。
動画のセグメンテーションが有効になっています。
各ビデオセグメントの配色を記述するJSONフィールドを出力するようにアナライザーを設定する必要があります。
アナライザーの設定方法を教えてください。適切な値を正しいターゲットにドラッグして設定します。各値は、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:
* Field method: generate
* Field value type: string
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The field should use the generate method because "describe the color scheme" asks Content Understanding to infer visual characteristics from each segment and generate a new semantic description. The desired value is not simply literal text that can be extracted from the media.
The field value type is string because a color-scheme description is textual-for example, " warm red and orange palette " or " predominantly cool blue and gray tones " .
Microsoft ' s Content Understanding video documentation explains that after segmentation, the generative- model layer can interpret scenes and populate developer-defined fields for every segment. Field schemas define a field ' s name, type, and description, and generated fields can capture semantic information derived from the source content. Microsoft Learn Therefore, the two values shown in the supplied answer image are reversed. The technically correct configuration is:
method = generate
type = string
This directly aligns with AI-103 ' s computer-vision objectives to implement visual understanding by configuring Azure Content Understanding to extract visual characteristics and process and interpret video segments . Microsoft Learn
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