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Microsoft Azure AI Fundamentals : AI-900 Exam

AI-900 Exam Questions
  • Exam Code: AI-900
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Aug 05, 2026
  • Q & A: 336 Questions and Answers
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About Microsoft AI-900 Exam Braindumps

How to Prepare For AI-900: Microsoft Azure AI Fundamentals

Preparation Guide for AI-900: Microsoft Azure AI Fundamentals

Introduction

Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. The objective of the certification is to verify that approved partners have the knowledge and skills to deploy, configure, set up, activate, maintain, troubleshoot, and support all necessary Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam taker level of competence in the functions, features, and services of Azure. The Microsoft AI-900 Certification Exam is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017, and NET Framework. Courses are available online or in-person depending on individual needs.

This article will cover the areas of knowledge required for this exam, how many questions are covered in each area, and who is taking this certification exam.

Requisites of Microsoft AI-900 Exam

In order to take the Microsoft AI-900 exam, you must have a current Azure subscription. You also need a valid Microsoft ID. In addition, be sure to pass the following exams:

  • AZ-100: Fundamentals of Azure
  • AZ-200: Azure Fundamentals Assessment

In case you're eager to get to know more about artificial intelligence facets & machine learning functions that relate to Microsoft Azure, then you're recommended to opt for AI-900 test.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Fundamental principles of machine learning on Azure15–20%- Describe core concepts of machine learning
- Describe capabilities of Azure Machine Learning
- Describe machine learning pipelines
- Describe automated machine learning
Features of generative AI workloads on Azure20–25%- Describe capabilities of Azure OpenAI Service
- Describe generative AI concepts
- Describe use cases for generative AI
- Describe responsible AI practices for generative AI
Artificial Intelligence workloads and considerations15–20%- Describe responsible AI principles
- Describe considerations for developing AI solutions
- Identify types of AI workloads
Features of Natural Language Processing (NLP) workloads on Azure15–20%- Identify types of NLP solutions
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
- Describe capabilities of Azure Translator
Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Custom Vision
- Identify types of computer vision solutions
- Describe capabilities of Azure Face
- Describe capabilities of Azure Computer Vision
- Describe capabilities of Azure Form Recognizer

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