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

  • Exam Code: AI-900J
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
  • Updated: Jul 29, 2026
  • Q & A: 336 Questions and Answers

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Prerequisites

Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.

The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.

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Prerequisites

This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.

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

Microsoft AI-900日本語 Exam Syllabus Topics:

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

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