Pass your test with the help of Microsoft AI-900 practice pdf. Prep4King offer 100% guarantee!
Last Updated: Jul 27, 2026
No. of Questions: 336 Questions & Answers with Testing Engine
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The Microsoft AI-900 exam is suitable for individuals who want to start a career in AI or individuals who want to expand their knowledge on AI and its implementation on Azure. AI-900 exam is also suitable for IT professionals who want to gain an understanding of AI and its potential applications in their organization. AI-900 exam is an excellent opportunity for those who want to showcase their skills and expertise in AI and Azure.
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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 certification exam is an excellent starting point for individuals who are interested in pursuing a career in AI or who want to demonstrate their expertise in the field. It covers a wide range of topics related to AI and the Azure platform and provides a valuable credential that can help individuals stand out in the job market.
Microsoft AI-900 is a foundational-level certification exam. Therefore, the potential candidates are required to possess basic knowledge of the AI and ML concepts. They also need to have an understanding of the associated Microsoft Azure services. These individuals can have technical or non-technical backgrounds. They do not need to have software engineering or science experience before taking the test. However, some programming experience or knowledge would be an advantage for them.
The AI-900 exam is designed to validate the students’ knowledge of AI workloads & considerations, as well as attributes of computer vision workloads within Azure. It also certifies their understanding of the basic principles of ML on Azure and attributes of conversational Artificial Intelligence workloads on Azure. The applicants are also required to demonstrate their knowledge of the attributes of NLP (Natural Language Processing) workloads on Azure.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 2: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Topic 3: Describe features of Generative AI workloads on Azure | 15-20% | - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI - Describe generative AI concepts |
| Topic 4: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe features of no-code automated ML - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities |
| Topic 5: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Describe Azure capabilities for NLP - Identify common NLP tasks - Identify Azure AI services for NLP |
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