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Microsoft Azure AI Fundamentals (AI-900日本語版) : AI-900日本語 Exam

AI-900日本語 actual test
  • Exam Code: AI-900J
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
  • Updated: Sep 14, 2026
  • Q & A: 336 Questions and Answers
  • PDF Demo
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About Microsoft Azure AI Fundamentals (AI-900日本語版) : AI-900日本語 Exam

The Microsoft AI-900 exam will measure the candidates’ skills and competence in a range of topics. They are as follows:

  • Explain the Features of Conversational Artificial Intelligence Workloads Available on Azure (15-20%): The applicants must demonstrate the understanding of common use cases associated with conversational artificial intelligence. This area also measures one’s knowledge of Azure services associated with conversational artificial intelligence.
  • Explain the Features of NLP (Natural Language Processing) Workloads Available on Azure (15-20%): This subject area will measure your ability to identify the features of basic Natural Language Processing Workload scenarios. It will also test your skills in identifying different Azure services and tools for NLP workloads. The topic will cover the understanding of the capabilities of Text Analytics service, Language Understanding service, Speech service, and Translator Text service.
  • Explain AI Workloads & Considerations (15-20%): This section will measure the individuals’ ability to identify different features of common artificial intelligence workloads. It will also evaluate their competence in identifying the guiding principles that are responsible for AI.
  • Explain the Features of Computer Vision Workloads Available on Azure (15-20%): This domain requires that the test takers demonstrate competence in identifying the basic categories of computer vision solutions. It will also measure their skills in identifying different Azure services and tools for computer vision tasks. You will also need an understanding of the capabilities of Computer Vision service, Custom Vision service, Face service, and Form Recognizer service.
  • Explain the Fundamental Principles of ML on Azure (30-35%): The potential candidates for the Microsoft AI-900 exam should be able to identify the common types of machine learning and explain its core concepts. They also need to know how to identify the core tasks that are involved in creating the ML solutions. Additionally, they need to have the knowledge of the capabilities of no-code ML with Azure ML studio.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

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Describing AI-900 Exam Facts and Topics

In general, AI-900 is a computer-based exam that’s available in English, Chinese, Japanese, Spanish, French, German, and Korean languages. It costs $99 for every attempt and can be scheduled with Certiport or Pearson VUE. Recently, the AI-900 exam curriculum has been updated to reflect the following skills as contained in the official study guide:

  • Explaining features of computer vision workloads on Azure (15-20%);
  • Detailing Artificial Intelligence considerations and workloads (15-20%);
  • Describing fundamental principles of machine learning on Azure (30-35%);
  • Defining features of conversational AI workloads on Azure (15-20%).
  • Designing features of NLP (Natural Language Processing) workloads on Azure (15-20%);

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Explain Computer Vision Workloads Features on Azure (15-20%)

Here, the following skills will be measured:

  • Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
  • Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.

Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Describe features of Generative AI workloads on Azure15-20%- Describe Azure OpenAI Service capabilities
- Describe generative AI concepts
- Identify responsible AI considerations for generative AI
Topic 2: Describe features of computer vision workloads on Azure15-20%- Identify Azure AI services for computer vision
- Identify common computer vision tasks
- Describe Azure capabilities for computer vision
Topic 3: Describe features of Natural Language Processing (NLP) workloads on Azure15-20%- Identify common NLP tasks
- Describe Azure capabilities for NLP
- Identify Azure AI services for NLP
Topic 4: Describe AI workloads and considerations15-20%- Identify guiding principles for responsible AI
- Identify features of common AI workloads
Topic 5: Describe fundamental principles of machine learning on Azure30-35%- Describe Azure Machine Learning capabilities
- Identify common machine learning tasks
- Describe core machine learning concepts
- Describe features of no-code automated ML

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