Updated: Aug 31, 2026
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The individuals who passed the Microsoft AI-900 exam possess the foundational knowledge of artificial intelligence (AI) and machine learning (ML). The test is designed to evaluate the candidates’ understanding of basic AI and ML workloads as well as how to execute them on Microsoft Azure. The students can earn the Microsoft Certified: Azure AI Fundamentals certification at the end of their journey with this path.
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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Certificate Validity Period: | Valid indefinitely |
| Exam Format: | Multiple select, Multiple choice, Scenario-based questions |
| Exam Duration: | 45 minutes |
| Related Certifications: | Microsoft Certified: Azure Data Fundamentals Microsoft Certified: Azure Fundamentals |
| Available Languages: | Chinese (Traditional), Italian, Spanish, Chinese (Simplified), French, Portuguese (Brazil), Indonesian (Indonesia), Russian, Arabic (Saudi Arabia), Korean, English, German, Japanese |
| Passing Score: | 700 (on a scale of 1–1000) |
| Exam Price: | $99 USD |
| Real Exam Qty: | 40–60 |
| Recommended Training: | Microsoft Learn: Azure AI Fundamentals Learning Path Instructor-led Training: AI-900 Course |
| Exam Registration: | Pearson VUE Registration Microsoft Certification Registration |
| Sample Questions: | Microsoft AI-900日本語 Sample Questions |
| Exam Way: | Online proctored or onsite testing at Pearson VUE test centers |
| Pre Condition: | No required prerequisites; basic familiarity with cloud computing or AI concepts is recommended but not mandatory |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/ |
The exam cost of Microsoft Azure AI Fundamentals is 99 USD.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
The average salary of an AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 3: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 4: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Identify types of computer vision solutions - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision |
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