Microsoft AI-900日本語 : Microsoft Azure AI Fundamentals (AI-900日本語版)
- Exam Code: AI-900日本語
- Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
- Updated: Sep 20, 2026 Q&As: 336 Questions and Answers
About Microsoft AI-900日本語 Exam
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Exam Details
The Microsoft AI-900 exam is the only qualifying test that leads to the Microsoft Certified: Azure AI Fundamentals certification. It measures one’s knowledge of AI considerations & workloads, features of NLP workloads on Azure, and basic principles of ML on Azure. The candidates can choose to take this exam in English, French, Simplified Chinese, Japanese, German, Spanish, or Korean. It contains 40-60 questions covering different types, including case studies, drag and drop, active screen, build list, and multiple choice. You can schedule your test with Certiport or Pearson VUE. However, it is required to pay the fee of $99 before scheduling it.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
What is the duration, language, and format of AI-900: Microsoft Azure AI Fundamentals Exam
- Passing Score: 70%
- Languages in which this exam is available in: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
- Type of Questions: This test format is multiple choice
- Length of Examination: 50 mins
- Number of Questions: 100 to 120 questions(Since Microsoft does not publish this information, the number of exam questions may change without notice)
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Microsoft AI-900日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Language |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines |
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