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

  • Exam Code: AI-900J
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900日本語版)
  • Updated: Jul 23, 2026
  • Q & A: 336 Questions and Answers

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

Requisites of Microsoft AI-900 Exam

In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:

  • AZ-200: Azure Fundamentals Assessment
  • AZ-100: Fundamentals of Azure

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

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The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.

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Certification Topics of Microsoft AI-900 Exam

Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.

  • Describe features of computer vision workloads on Azure (15-20%)
  • Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
  • Describe features of conversational AI workloads on Azure (15-20%)
  • Describe AI workloads and considerations (15-20%)
  • Describe fundamental principles of machine learning on Azure (30-35%)

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Microsoft AI-900日本語 Exam Syllabus Topics:

SectionWeightObjectives
Features of computer vision workloads on Azure15–20%- Identify types of computer vision solutions
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Face
- Describe capabilities of Azure Form Recognizer
Features of generative AI workloads on Azure20–25%- Describe responsible AI practices for generative AI
- Describe generative AI concepts
- Describe use cases for generative AI
- Describe capabilities of Azure OpenAI Service
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Language
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Translator
- Identify types of NLP solutions
Artificial Intelligence workloads and considerations15–20%- Describe considerations for developing AI solutions
- Identify types of AI workloads
- Describe responsible AI principles
Fundamental principles of machine learning on Azure15–20%- Describe automated machine learning
- Describe core concepts of machine learning
- Describe machine learning pipelines
- Describe capabilities of Azure Machine Learning

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