Microsoft Azure AI Fundamentals (AI-900 Korean Version) : AI-900 Korean

AI-900 Korean Exam Simulator
  • Exam Code: AI-900-KR
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Updated: Sep 06, 2025
  • Q & A: 321 Questions and Answers

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About Microsoft AI-900 Korean Exam Braindumps

Certification Topics of Microsoft AI-900 Exam

Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.

  • Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
  • Describe AI workloads and considerations (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 conversational AI workloads on Azure (15-20%)

In case you're eager to get to know more about artificial intelligence facets & machine learning functions that relate to Microsoft Azure, then you're recommended to opt for AI-900 test.

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

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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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Microsoft AI-900 Korean exam simulator

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