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Microsoft AI-103日本語 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Apps and Agents on Azure |
| Exam Number: | AI-103 |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026) |
| Passing Score: | 700 / 1000 |
| Exam Format: | Scenario-based, Case study, Multiple choice, Performance-based items |
| Exam Duration: | 100 minutes |
| Available Languages: | Portuguese (Brazil), German, Chinese (Simplified), English, Spanish, French, Korean, Japanese |
| Real Exam Qty: | 50–60 |
| Certificate Validity Period: | 1 year (renewable annually via free online assessment) |
| Exam Price: | $165 USD (varies by country/region) |
| Recommended Training: | Azure AI Foundry Documentation Microsoft Learn: Develop AI Apps and Agents on Azure |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Dashboard |
| Sample Questions: |  |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-103 |
Microsoft AI-103日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 2: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 3: Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Topic 4: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
Microsoft AI-103日本語 Exam: Full-Scale Answers
No mandatory prerequisites; recommended experience: 6+ months building AI applications, proficiency in Python or C#, familiarity with Azure services and AI concepts Eligibility rules change over time, so verify the current requirements on the official page (official AI-103日本語 exam page) before registering.
The Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) is Microsoft's certification exam for Microsoft Certified: Azure AI Apps and Agents Developer Associate, at the Associate level. Related credentials include Microsoft Certified: Azure AI Engineer Associate (retiring June 30, 2026). Start simply: free demo first, full set when convinced.
Yes:
After any course, reinforce it with the 152 practice questions for the Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) — every answer expert-verified.
100 minutes for 50–60 questions. The GetValidTest engine simulates real examination conditions, so the pacing feels familiar before exam day.
Yes — download our demo freely as your reference; you may be impressed by the conciseness and clearness of the Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) exam VCE. Purchases include 365 days of free updates by email; renew afterward at 50% off.
The Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) blueprint spans 5 domains — including Implement information extraction and knowledge mining (10–15%), Implement text and speech analysis solutions (10–15%), Implement generative AI and agentic solutions (30–35%). Our material keeps close to this syllabus; the complete outline above lists every subtopic.
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Microsoft Developing AI Apps and Agents on Azure (AI-103日本語版) Sample Questions:
エージェントとイメージ生成モデルのデプロイメントを含む Microsoft Foundry プロジェクトがあります。
このエージェントは、ユーザーが提供した商品写真からオリジナルの画像を生成します。
生成された画像が、提供された写真の製品アイデンティティと視覚的特徴を維持し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. 接地検出フィルターを適用します。
- B. リクエストにプロンプトと入力画像を含めます。
- C. 温度パラメータの値を減らします。
- D. input_fidelity パラメータを high に設定します。
Correct Answer: D 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
モデルを再学習させたり、知識を永続的に保存したりすることなく、正しい分類がどのようなものかモデルに示すことで、分類精度を向上させる必要があります。
- A. 検索拡張生成(RAG)
- B. ゼロショット学習
- C. 思考の連鎖
- D. 少数ショット学習
Correct Answer: D 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
エージェントがターンやセッションをまたいで完全なインタラクション履歴を自動的に再利用できるように、永続的なIDを使用して状態を保持する必要があります。
- A. 応答
- B. エージェント
- C. 出力項目
- D. 会話
Correct Answer: D 🗳️
Explanation: Only visible for GetValidTest members. You can sign-up / login (it's free).
あなたは食品用の段ボール包装材を製造する工場を経営しています。その工場はインターネット接続が不安定です。
パッケージには、各製品のサンプルを4つずつ含める必要があります。
パッケージの欠陥を特定し、その欠陥箇所をオペレーターに提供するAzure Custom Visionモデルを構築する必要があります。このモデルは、各パッケージに4つの製品がすべて含まれていることを保証するものでなければなりません。
どのプロジェクトタイプとドメインを使用すべきでしょうか？回答するには、適切なオプションを正しいターゲットに移動してください。各オプションは、1回、複数回、またはまった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移動したり、スクロールしたり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:
The model must use object detection because the solution needs more than an overall classification. It must identify individual defects, provide their locations, and count individual product samples. Object-detection predictions contain bounding-box coordinates in addition to labels and confidence scores, allowing the application to determine where a defect occurred and whether exactly four product instances are present.
The appropriate domain is General (compact) . The packaging and products are organization-specific and do not map to a more specialized object-detection domain. More importantly, a compact domain supports model export for edge/local inference . This matters because the factory has intermittent internet connectivity; the exported model can execute locally rather than depending continuously on a hosted Azure prediction endpoint.
Microsoft documents compact domains as optimized for deployment outside the Custom Vision hosted service. Exportable formats can include ONNX, TensorFlow, TensorFlow Lite, and other target-specific formats depending on project type and domain.
An ordinary General-domain model is intended for Azure-hosted prediction and cannot satisfy the same offline export requirement. Image classification is also unsuitable because it cannot return defect locations or count independently detected products.
Study Guide alignment: identify objects/components/regions and deploy vision functionality appropriate for edge scenarios . Microsoft Learn
Agent1のモデル展開を技術要件を満たすように構成する必要があります。
何を設定すればよいですか？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:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
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