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Microsoft Certified: Azure AI Fundamentals AI-900 中文

AI-900 中文

Exam Code: AI-900-CN

Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)

Updated: Aug 26, 2026

Q&A Number: 336 Q&As

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Preparation Process

Microsoft offers a wide range of resources to help you prepare for your certification exam. The first step in the preparation process is to go through the topics in detail. This will enable you to understand the specific areas that are covered in the test. With this understanding, you can start to get the materials that will provide more knowledge on the objectives. Thus, Microsoft comes with two training options to help the candidates develop the required skills. These are free online training and instructor-led training.

  • Free Online Training: The individuals can explore five free training tutorials that are available on the official exam webpage. They have a series of modules that focus on the specific domains of the test. These training tutorials are recommended for those candidates who are comfortable with the self-paced study. The topics of these manuals include the following options:
    • Get Started with AI on Azure;
    • Create No-Code Predictive Models with Azure ML;
    • Explore NLP;
    • Explore Computer Vision on Microsoft Azure;
    • Explore Conversational Artificial Intelligence.

    The students can find the details of these learning paths on the official website.

  • Instructor-Led Training: The Microsoft Azure AI Fundamentals course is designed to help you gain the required skills to pass the exam and obtain the certification. It introduces the learners to the fundamental concepts of AI and the services available in Microsoft Azure that are useful in creating AI solutions. This training is delivered by the certified partners and its details can be found on the Microsoft website. It is important to mention that this course is designed to be combined with other online materials that are available on the Microsoft Learning platform.
  • Practice Test: As a future examinee, you also have access to the official practice test to evaluate your understanding of the exam topics before taking the actual test. The link to this training tool can be found on the Microsoft website.

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Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian

Retirement date: none

Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

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Skills measured

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

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

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

  • 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 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 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.
  • 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.

Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of machine learning on Azure25-30%- Core machine learning concepts
  • 1. Supervised vs unsupervised learning
    • 2. Training and validation concepts
      Topic 2: Features of natural language processing (NLP) workloads on Azure30-35%- Text analytics and language understanding
      • 1. Sentiment analysis
        • 2. Language modeling and translation
          • 3. Key phrase extraction
            Topic 3: Features of computer vision workloads on Azure15-20%- Computer vision solutions
            • 1. Object detection
              • 2. Image classification
                • 3. OCR and image analysis
                  Topic 4: Describe AI workloads and considerations20-25%- Fundamentals of artificial intelligence concepts
                  • 1. Common AI workloads
                    • 2. Responsible AI principles
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