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Updated: Sep 01, 2026
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Microsoft AI-900 Korean Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Related Certifications: | Microsoft Certified: Azure AI Fundamentals |
| Passing Score: | 700/1000 |
| Exam Duration: | 45-60 |
| Available Languages: | Indonesian, Japanese, Portuguese (Brazil), Chinese (Simplified), French, Korean, English, Spanish, German |
| Certificate Validity Period: | No expiration (certification does not expire) |
| Exam Format: | Single-choice, Multiple-choice, Sequence ordering, Drag and drop, True/False |
| Exam Price: | USD 99.00 |
| Real Exam Qty: | 40-60 |
| Sample Questions: | Microsoft AI-900 Korean Sample Questions |
| Exam Way: | Online proctored exam or in-person testing center (Pearson VUE) |
| Pre Condition: | No formal prerequisites required. Basic knowledge of cloud computing concepts is recommended but not mandatory. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/certifications/exams/ai-900/ |
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Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi Arabia), Chinese (Traditional), Italian
Retirement date: none
Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.
Important facts you need to know about AI-900: Microsoft Azure AI Fundamentals Exam
Preparing for the exam will help you screen out questions that are irrelevant for this certification. Ingestion of the data is the most important role in AI. Ready to use the data as soon as possible. Anomaly detection refers to all situations where something out of the ordinary is happening. Accountability models are used for the accuracy rate. Transparency refers to using the data for this service. Interoperability is used on the platform. Microsoft AI-900 exam dumps in order to get the best scores with the Microsoft Azure AI Fundamentals Exam. Tech terms used in AZ-900:Microsoft Azure AI Fundamentals Exam. Serviceidentify is used in the process of AI. Files are used to store the data. The AI application is the product of the AI. Learning is used for this purpose to provide better accuracy rate.
Concepts of the AI are explained in the Microsoft AI-900 exam. Image classification is used as the labeling. Recommendation engine is used as the indexing. Intelligent chatbots are used as the chatbot. Community engagement is the chatbot. Custom bot is used as the chatbot. Extracts are used for this purpose. Brainpool is the tool used to perform the extraction. Word embedding is used as the vector training. Dimensionality reduction is used for this purpose. Intelligent chatbot are used as the chatbot.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
The AI-900 exam covers a wide range of topics related to AI and machine learning, including the basics of AI, different types of AI, Azure AI services, computer vision, natural language processing (NLP), and conversational AI. AI-900-KR exam also covers ethical and responsible AI practices, including the ethical considerations of using AI, privacy, and security concerns.
Microsoft AI-900 Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Describe features of no-code automated ML - Identify common machine learning tasks |
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify Azure AI services for NLP - Identify common NLP tasks |
| Topic 3: Describe features of computer vision workloads on Azure | 15-20% | - Identify common computer vision tasks - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision |
| Topic 4: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify guiding principles for responsible AI - Identify features of common AI workloads |
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