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Last Updated: Sep 25, 2026

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Microsoft AI-900 certification exam, also known as the Microsoft Azure AI Fundamentals exam, is a fundamental-level exam that measures a candidate's knowledge and understanding of essential concepts related to artificial intelligence (AI) and machine learning (ML) on the Azure platform. Microsoft Azure AI Fundamentals (AI-900中文版) certification exam is intended for individuals who wish to demonstrate their knowledge of AI and ML concepts and their practical applications using the Azure platform. AI-900-CN exam is designed to validate that the candidate has the foundational knowledge required to use Azure AI services to build intelligent solutions.

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

Microsoft AI-900: Microsoft Azure AI Fundamentals is an exam that tests one's knowledge on the basics of Artificial Intelligence (AI) and its implementation on Microsoft Azure. AI-900-CN exam is an excellent starting point for those who want to explore the world of AI and its potential applications. AI is a rapidly growing field, and AI-900-CN exam provides an opportunity for individuals to gain a foundational understanding of AI and its impact on our daily lives.

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Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Training and validation concepts
    • 2. Supervised vs unsupervised learning
      Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
      • 1. Language modeling and translation
        • 2. Sentiment analysis
          • 3. Key phrase extraction
            Features of computer vision workloads on Azure15-20%- Computer vision solutions
            • 1. Object detection
              • 2. OCR and image analysis
                • 3. Image classification
                  Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
                  • 1. Common AI workloads
                    • 2. Responsible AI principles

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