Microsoft AI-900 中文 Q&A - in .pdf

  • Exam Code: AI-900 中文
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Sep 24, 2026
  • Q & A: 336 Questions and Answers
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Microsoft AI-900 中文 Q&A - Testing Engine

  • Exam Code: AI-900 中文
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Sep 24, 2026
  • Q & A: 336 Questions and Answers
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Learning Path

All in all, Microsoft provides two ways to help candidates prepare for the Microsoft AI-900 exam. These include the free online learning option and the paid, instructor-led training. Let’s discuss the two most popular courses offered by each of these options by the vendor:

  • Get Started with Artificial Intelligence

    The online gratis class, “Get Started with Artificial Intelligence” features one module and is intended to run for a maximum of 34 minutes. In general, this course is designed to help you get started with Microsoft services that empower new AI solutions. There are no prerequisites associated with this training. However, it features one module with a total of 10 units including the introduction and an understanding of machine learning, anomaly detection, computer vision, natural language processing, conversational AI, and responsible AI just to mention a few. Visit the official certification page to find out more information about this learning path that also includes classes on ML, NLP, and conversational AI.

  • Microsoft Azure AI Fundamentals

    This one-day course is part of the instructor-led learning platform and is, therefore, a paid option. As the name suggests, it is meant to introduce the candidates to fundamental AI concepts. The training, in particular, emphasizes the skills you need to become well versed in AI workloads and understand the Azure services that support such solutions. Usually, the course curriculum is viewed as comprehensive blended learning, featuring a combination of online materials and instructor-led training to ensure candidates have a good grasp of the exam topic areas. Even more, the hands-on exercises that are featured here are meant to help learners understand the test domains in real-world scenarios. Familiarity with the internet and computer technology is not a prerequisite but a great combination that will go a long way toward helping you register excellent results in this course and the ultimate exam.

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Exam Topics

The potential candidates for this certification test must develop their skills in its topics before attempting it. It is recommended to go through the official webpage to review the detailed objectives. Microsoft updates the topics of its exams regularly, so studying the latest domains is very important to your success in the test. Thus, Microsoft AI-900 covers the following sections:

Describe AI Considerations and Workloads (15-20%)

The first topic evaluates the following skills:

  • Identify the guiding principles associated with responsible Artificial Intelligence – This one will evaluate the candidates’ ability to explain some concepts. These include considerations of safety and reliability in AI solutions and considerations for fairness in AI solutions. It also covers considerations for security and privacy, transparency, inclusiveness, and accountability within an AI solution.
  • Identify the common AI Workloads features – You should be able to identify a variety of workloads, such as forecasting/prediction, computer vision, and conversational AI. The subsection also covers the characteristics of anomaly detection workloads, knowledge mining, and natural language processing workloads.

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

Microsoft AI-900 中文 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamental principles of machine learning on Azure15–20%- Describe core concepts of machine learning
- Describe machine learning pipelines
- Describe automated machine learning
- Describe capabilities of Azure Machine Learning
Topic 2: Artificial Intelligence workloads and considerations15–20%- Describe considerations for developing AI solutions
- Identify types of AI workloads
- Describe responsible AI principles
Topic 3: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Form Recognizer
- Identify types of computer vision solutions
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Face
Topic 4: Features of generative AI workloads on Azure20–25%- Describe responsible AI practices for generative AI
- Describe use cases for generative AI
- Describe generative AI concepts
- Describe capabilities of Azure OpenAI Service
Topic 5: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Speech
- Identify types of NLP solutions
- Describe capabilities of Azure Language
- Describe capabilities of Azure Translator

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