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

AI-900 Korean Exam Simulator
  • Exam Code: AI-900-KR
  • Exam Name: Microsoft Azure AI Fundamentals (AI-900 Korean Version)
  • Updated: Sep 09, 2026
  • Q & A: 336 Questions and Answers
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Prerequisites

Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.

The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.

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Exam AI-900: Microsoft Azure AI Fundamentals

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Part of the requirements for: Microsoft Certified: Azure AI Fundamentals

Download exam skills outline

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

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
Features of computer vision workloads on Azure15–20%- Identify types of computer vision solutions
- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Face
- Describe capabilities of Azure Custom Vision
- Describe capabilities of Azure Computer Vision
Artificial Intelligence workloads and considerations15–20%- Identify types of AI workloads
- Describe responsible AI principles
- Describe considerations for developing AI solutions
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Identify types of NLP solutions
- Describe capabilities of Azure Language
- Describe capabilities of Azure Translator
- Describe capabilities of Azure Speech
Features of generative AI workloads on Azure20–25%- Describe use cases for generative AI
- Describe generative AI concepts
- Describe capabilities of Azure OpenAI Service
- Describe responsible AI practices for generative AI
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

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