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

Microsoft AI-900 Korean Actual PDF
  • Exam Code: AI-900-KR
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Updated: Aug 15, 2026
  • Q & A: 336 Questions and Answers
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About Microsoft AI-900 Korean Exam

Studying with Revision Books

Apart from the training endorsed by the certification vendor, it’s a good idea to exhaust the study guides from Amazon. Thus, here’s what you need to know about the two must-have revision books for the Microsoft AI-900 certification exam prep:

  • Microsoft AI MVP Book: Practical Guide to Microsoft AI Written by 17 AI and Azure MVPs from all around the world

    This book is a collection of skills and knowledge from a team of AI and Azure specialists with countless years of experience in the field. It is written by Microsoft Azure MVPs including Leila Etaati, Anupama Natarajan, Ashraf Ghonaim, Cameron Vetter, David Rendon, etc. who used their cumulative years of experience to design a definitive study guide and a solution for all trainees preparing for AI-900 exam. The authors state it categorically that this book does not intend to cover everything there’s to know about Microsoft AI. And that it is important to combine it with other study materials to have a better chance of passing the Microsoft AI-900 exam. In a nutshell, this guide targets AI and ML developers, architects, data scientists, .net developers, and consultants who are already familiar with the foundational AI concepts. Remember it is not your best guide if you are starting from a zero, but an excellent choice if you are already familiar with most of the concepts in this field.

  • Azure AI Fundamentals: Study Guide and Practice Exam for Microsoft AI-900 Exam

    This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.

Certification Path

The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.

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

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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 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.
  • 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.

Microsoft AI-900 Korean Exam Syllabus Topics:

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

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