Microsoft AI-900日本語 : Microsoft Azure AI Fundamentals (AI-900日本語版)
- Exam Code: AI-900J
- Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
- Updated: Oct 26, 2026
- Q & A: 336 Questions and Answers
About Microsoft AI-900日本語 Exam Questions
Bottom Line
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 Practice Test Questions, Microsoft AI-900 Exam Practice Test Questions
The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
Microsoft Azure AI Fundamentals (AI-900日本語版) Sample Questions:
Microsoft Foundryを使用して、自動車のナンバープレートを読み取るAIアプリケーションを開発する予定です。このアプリケーションの開発には、何を使用すべきでしょうか？
- A. Microsoft Visual Studio Code
- B. GitHub Actions
- C. Microsoft 365 用 Copilot
- D. Azure Al Studio
Correct Answer: D 🗳️
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
Safety system.
According to the Microsoft Learn documentation and the AI-900: Microsoft Azure AI Fundamentals official study guide, the safety system layer in generative AI architecture plays a crucial role in monitoring, filtering, and mitigating harmful or unsafe model outputs. This layer works alongside the model and user experience layers to ensure that generative AI systems-such as those powered by Azure OpenAI-produce responses that are safe, aligned, and responsible.
The safety system layer uses various techniques including content filtering, prompt moderation, and policy enforcement to prevent outputs that could be harmful, biased, misleading, or inappropriate. It evaluates both user inputs (prompts) and model-generated outputs to identify and block unsafe or unethical content. The system might use predefined rules, classifiers, or human feedback signals to decide whether to allow, modify, or stop a response.
In contrast, the other layers serve different purposes:
* The model layer contains the core large language or generative model (e.g., GPT or DALL-E) that processes inputs and produces outputs.
* The metaprompt and grounding layer ensures the model's responses are contextually relevant and factually supported, often linking to organizational data sources or system prompts.
* The user experience layer defines how users interact with the AI system, including the interface and conversational flow, but does not manage safety enforcement.
Therefore, the layer that uses system inputs and context to mitigate harmful outputs from a generative AI model is the Safety system layer.
This aligns with Microsoft's responsible AI principles-Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency, and Accountability-ensuring generative AI operates ethically and safely.
文を正しく完成させる答えを選択してください。
Correct Answer:
Explanation:
The correct answer is Azure AI Custom Vision.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore computer vision in Azure", the Azure AI Custom Vision service is specifically designed to allow users to train, test, and deploy custom image classification and object detection models using their own images.
The Azure AI Custom Vision service extends the capabilities of Azure's general-purpose Computer Vision API by enabling organizations to upload their own labeled datasets, define custom tags (labels), and train models that are optimized for their specific use cases. This makes it ideal for object detection scenarios-such as detecting equipment in a manufacturing line, identifying products on store shelves, or recognizing medical images-where general-purpose models may not suffice.
By contrast, the Azure AI Computer Vision service provides pre-built models for tasks such as image description, tagging, face detection, and OCR (optical character recognition). It does not allow users to train their own models with custom data. Similarly, Azure AI Document Intelligence is used for extracting structured information from documents (forms, receipts, invoices), and Azure Video Analyzer for Media focuses on analyzing video content for insights and metadata extraction.
The AI-900 study guide emphasizes that the Custom Vision service supports two key model types:
* Image Classification - categorizing entire images based on predefined tags.
* Object Detection - identifying and locating multiple objects within an image by drawing bounding boxes.
Therefore, when the question specifies "train an object detection model by using your own images," the correct Azure service is Azure AI Custom Vision, as it provides the necessary tools for training, evaluating, and deploying custom computer vision models tailored to a user's dataset.
Hence, the verified correct answer is: Azure AI Custom Vision.
Azureで自然言語処理ソリューションを開発しています。このソリューションは、顧客のレビューを分析し、各レビューがどれほど肯定的か否定的かを判断します。
これは、どのタイプの自然言語処理ワークロードの例ですか？
- A. キーフレーズ抽出
- B. 感情分析
- C. 言語検出
- D. エンティティの認識
Correct Answer: B 🗳️
Explanation: Only visible for Exams4Collection members. You can sign-up / login (it's free).
コンピューター ビジョンのワークロードの種類を適切なシナリオに合わせます。
答えるには、適切なワークロード タイプを左側の列から右側のシナリオにドラッグします。各ワークロード タイプは複数回使用されることも、まったく使用されないこともあります。
注: 正しく一致するたびに 1 ポイントの価値があります。
Correct Answer:
Explanation:
In the Microsoft Azure AI Fundamentals (AI-900) curriculum, computer vision workloads are grouped into distinct types, each serving a specific purpose. The three major workloads illustrated here are image classification, object detection, and optical character recognition (OCR). Understanding their use cases is essential for correctly mapping them to real-world scenarios.
* Generate captions for images # Image classificationThe image classification workload is used to identify the main subject or context of an image and assign descriptive labels. In Microsoft Learn's
"Describe features of computer vision workloads," image classification models are trained to recognize content (e.g., a cat, a beach, or a city). Caption generation expands on classification results by describing the image's contents in human-readable language-based on what the model identifies as key visual features.
* Extract movie title names from movie poster images # Optical character recognition (OCR)OCR is a vision workload that detects and extracts text from images. Azure AI Vision's Read API or Document Intelligence OCR models can identify printed or handwritten text within posters, signs, or documents.
In this case, the movie title text from a poster is best extracted using OCR.
* Locate vehicles in images # Object detectionThe object detection workload identifies multiple objects within an image and provides their locations using bounding boxes. It's ideal for tasks like counting cars in a parking lot or tracking objects in traffic images.
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