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

  • Exam Code: AI-900-CN
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900中文版)
  • Updated: Sep 12, 2026
  • Q&As: 336 Questions and Answers

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Details on Azure services and differences among them. Documents that are critical to the success of the exam. Environment to take the exam in. Sufficient understanding of Azure and its services. High level of difficulty and many challenging questions. Sit for the exam to prove your skills. Affiliate marketing and earn money for yourself. Explanations that demonstrate your understanding. Receive a valuable document to help you pass the exam. Reliability of the exam. Desktop, laptop, and mobile devices. Subscribe to receive the latest study materials. Ensure you are comfortable with the exam environment. Formats suited for different learning styles.

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Test AI-900 Scope of Knowledge

Overall, AI-900 exam tests the applicant's understanding of AI and ML through various topics categorized into 5 domains:

  • Fundamental principles of machine learning on Azure (30-35%)

    In the second section, the applicant's knowledge of fundamental principles of machine learning in relation to Azure cloud computing services will be tested. This requires the applicant to have a clear comprehension of all the common types of various scenarios for ML that include classification, regression, and clustering. It is also essential to have a thorough understanding of the core MI concepts such as labels & features in datasets, dataset training that go with the validation process, etc. The entrant must as well as be capable of identifying the core task required in developing an ML solution. So, understanding the ingestion and preparation of data chunks, selection and engineering of various features, evaluation procedure as well as training for models, and deployment & management of those models will be vital. Finally, this portion of AI-900 exam includes concepts of ML automated user interface that are ML Wizard UI and Azure ML designer.

  • Conversational workloads for AI based on Azure and features (15-20%)

    The fifth and final domain for such an exam tests the entrant's understanding of conversational AI workloads. This requires the comprehension of facets as well as cases for the usage of conversational traits for AI which includes web chatbots, voice menus for the telephone, and digital assistants used for personal purposes. The applicant also needs to know how to check various Azure services related to conversational AI. Additionally, they should have a firm understanding of the QnA Maker Service & Bot service. Finally, it is essential for candidates to know and be able to describe the common characteristics of conversational artificial intelligence solutions.

  • AI workloads and considerations (15-20%)

    The first topic of the final exam deals with AI workloads that are usually common. It includes candidates' understating various types of workloads such as predicting forecasting traits, the detection of anomalies, vision for computer systems, NLP, and conversational AI workloads. The examinees are also required to have a thorough understanding of the principles of AI that possess responsibility traits. It means that the candidate needs to be well aware of all the considerations for different solutions for AI which include fairness, reliability, security, privacy, inclusiveness, transparency facets, and finally, functions for accountability.

  • Workloads for computer vision based on Azure and features (15-20%)

    This segment of the official test is all about workloads for computer vision in relation to Azure services. To get through this part, one must have a general grasp of the various common forms of computer vision solutions that include image classification, object detection, semantic segmentation, recognition for the optical character, and facial detection. The applicants are also required to have an understanding of the diverse methods including services used in Azure for activities that utilize computer vision. Such methods and services include the vision for computers, vision for custom, services for face vision, and at last, the recognizer of the form.

  • Workloads for Natural Language Processing (NLP) related to Azure (15-20%)

    The fourth portion of AI-900 exam deals with Natural Language Processing or NLP workloads in Azure. To get a passing score, the entrant must have a firm grasp on the facets and usage of diverse NLP scenarios for workloads which comprise the extraction of the key phrase, the recognition of entities, the analysis of sentiments, modeling for languages, the recognition of speech, and translation traits. The applicants are also required to know about different techniques as well as services of Azure used for various NLP workloads. They are required to be capable of identifying diverse abilities of various services such as text analysis, LUIS, speech, and translator text.

For more info read reference:

Microsoft AI-900 Official Certification Learning Site

Training and Certification Guide

Exam policies and FAQs

The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.

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

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