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

AI-900 中文 real exams

Exam Code: AI-900-CN

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

Updated: Jul 27, 2026

Q & A: 336 Questions and Answers

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Microsoft Azure AI Fundamentals Exam Certification Details:

Sample QuestionsMicrosoft Azure AI Fundamentals Sample Questions
Schedule ExamPearson VUE
Exam CodeAI-900
Exam NameMicrosoft Certified - Azure AI Fundamentals
Duration60 mins
Exam Price$99 (USD)
Number of Questions40-60
Passing Score700 / 1000
Books / TrainingCourse AI-900T00: Microsoft Azure AI Fundamentals

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

Microsoft AI-900 Exam Syllabus Topics:

TopicDetails

Describe Artificial Intelligence workloads and considerations (20-25%)

Identify features of common AI workloads- identify features of anomaly detection workloads
- identify computer vision workloads
- identify natural language processing workloads
- identify knowledge mining workloads
Identify guiding principles for responsible AI- describe considerations for fairness in an AI solution
- describe considerations for reliability and safety in an AI solution
- describe considerations for privacy and security in an AI solution
- describe considerations for inclusiveness in an AI solution
- describe considerations for transparency in an AI solution
- describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (25-30%)

Identify common machine learning types- identify regression machine learning scenarios
- identify classification machine learning scenarios
- identify clustering machine learning scenarios
Describe core machine learning concepts- identify features and labels in a dataset for machine learning
- describe how training and validation datasets are used in machine learning
Describe capabilities of visual tools in Azure Machine Learning studio- automated machine learning
- azure Machine Learning designer

Describe features of computer vision workloads on Azure (15-20%)

Identify common types of computer vision solution- identify features of image classification solutions
- identify features of object detection solutions
- identify features of optical character recognition solutions
- identify features of facial detection, facial recognition, and facial analysis solutions
Identify Azure tools and services for computer vision tasks- identify capabilities of the Computer Vision service
- identify capabilities of the Custom Vision service
- identify capabilities of the Face service
- identify capabilities of the Form Recognizer service

Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)

Identify features of common NLP Workload Scenarios- identify features and uses for key phrase extraction
- identify features and uses for entity recognition
- identify features and uses for sentiment analysis
- identify features and uses for language modeling
- identify features and uses for speech recognition and synthesis
- identify features and uses for translation
Identify Azure tools and services for NLP workloads- identify capabilities of the Language service
- identify capabilities of the Speech service
- identify capabilities of the Translator service
Identify considerations for conversational AI solutions on Azure- identify features and uses for bots
- identify capabilities of the Azure Bot service

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