Last Updated: Sep 06, 2026
No. of Questions: 336 Questions & Answers with Testing Engine
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.
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The benefit that you receive after passing the AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules is greater than the effort which you put in to pass this exam. Provide you a growth in terms of your knowledge and will open many opportunities for you. Sentiment of the IT companies will increase because of your skilled work. Candidates who are verified with the AI-900: Microsoft Azure AI Fundamentals Exam will get an advantage in job interviews. Sites such as Microsoft Learning comes with a collection of certifications, tips and articles which you can use to prepare for the exam.
Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.
Guides, strategies and testimonials you can find on the internet about the exam. Helpful to deal with the challenges in the exam. Attempt to use the free copies of the Microsoft Office Excel for this exam. Modeling approach for this exam is to use the AI technology through an artificial neural network.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Identify types of NLP solutions - Describe capabilities of Azure Language - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech |
| Topic 2: Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe core concepts of machine learning - Describe automated machine learning - Describe machine learning pipelines |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Custom Vision - Identify types of computer vision solutions |
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