Microsoft AI-900 Exam Details & Actual Exam Questions
- Exam Code/Number: AI-900
- Exam Name/Title: Microsoft Azure AI Fundamentals
- Certification Provider: Microsoft
- Corresponding Certification: Microsoft Certified: Azure AI Fundamentals
- Exam Questions: 336
- Updated On: Sep,05 2026
- Certification Level: Fundamental
Microsoft Azure AI Fundamentals Exam Questions
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(43 Up Votes)Microsoft AI-900 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Azure AI Fundamentals |
| Exam Number: | AI-900 |
| Exam Duration: | 45 minutes |
| Available Languages: | Korean, French, Chinese (Traditional), Japanese, Arabic (Saudi Arabia), Indonesian (Indonesia), German, Portuguese (Brazil), English, Spanish, Chinese (Simplified), Russian, Italian |
| Related Certifications: | Microsoft Certified: Azure Data Fundamentals Microsoft Certified: Azure Fundamentals |
| Passing Score: | 700 (on a scale of 1–1000) |
| Exam Format: | Multiple select, Multiple choice, Scenario-based questions |
| Exam Price: | $99 USD |
| Certificate Validity Period: | Valid indefinitely |
| Real Exam Qty: | 40–60 |
| Recommended Training: | Instructor-led Training: AI-900 Course Microsoft Learn: Azure AI Fundamentals Learning Path |
| Exam Registration: | Microsoft Certification Registration Pearson VUE Registration |
| Sample Questions: | Microsoft AI-900 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/ |
Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe generative AI concepts |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Computer Vision - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |
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