Last Updated: Sep 13, 2026
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The individuals can pursue a lot of career opportunities with a Microsoft certificate. The Microsoft Certified: Azure AI Fundamentals certification is not an exception. Although it is a foundational-level path, the certificate holders can start their careers on a good footing. Some of the job titles that these entry-level professionals can apply for include an Azure Data Scientist, an Azure AI Engineer, an Azure Administrator, an Azure Developer, an Azure Solution Architect, and an Azure DevOps Engineer. For these positions, the applicants can expect to earn an average of $80,000 per annum.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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The interested candidate for the Microsoft AI-900 test, which also goes under the name the Azure test for AI Fundamentals, should have at least some foundational understanding of the concept of AI features alongside ML ones. This exam will test the entrant's knowledge of different AI common workloads, principles of AI and ML, NLP workloads, and other related concepts. When it comes to the details for this exam, the Microsoft AI-900 is available in several languages, among which you'll find Spanish, English, Korean, German, Chinese (Simplified), and French. Such an exam comes with a registration fee of $99 and should be scheduled through Pearson VUE. Also, in the main exam, candidates will be exposed to around 40-60 tasks with 1 hour given to solve them.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - 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 |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Topic 5: 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 |
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