Microsoft AI-900 中文 real exam prep : Microsoft Azure AI Fundamentals (AI-900中文版)

  • Exam Code: AI-900-CN
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Aug 02, 2026
  • Q&As: 336 Questions and Answers

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Microsoft AI-900 Practice Test Questions, Microsoft AI-900 Exam Practice Test Questions

The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.

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

Describe the Basic Principles of ML on Microsoft Azure (30-35%)

As for this area, it includes the following:

  • Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
  • Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.
  • Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
  • Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.

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Microsoft AI-900 中文 Exam Syllabus Topics:

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

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