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Microsoft Azure AI Fundamentals : AI-900

AI-900 Exam Simulator
  • Exam Code: AI-900
  • Exam Name: Microsoft Azure AI Fundamentals
  • Updated: Aug 10, 2026
  • Q & A: 336 Questions and Answers
  • Microsoft AI-900 Q&A - in .pdf

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About Microsoft AI-900 Exam Simulator

Prerequisites

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.

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.

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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.

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

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About Azure AI Fundamentals Certification and Who It Is Intended for

The Microsoft Certified: Azure AI Fundamentals qualification authenticates the learner's understanding and skills in AI & ML concepts along with associated Azure services. In particular, the individual with this certification is competent in demonstrating their understanding of various workloads related to those terms along with their implementation in Azure cloud computing services. In addition, this certification is an excellent way to get a step ahead in the preparation for other role-based Azure certificates as well as showcasing one's skill in the fundamentals of ML as well as AI. All in all, the Azure AI Fundamentals validation form Microsoft is for beginners and thus, doesn't require any technical knowledge and expertise like, for example, engineering for software. However, some understanding of programming will help you a lot to easily nail test AI-900.

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Microsoft AI-900 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamental principles of machine learning on Azure15–20%- Describe machine learning pipelines
- Describe core concepts of machine learning
- Describe automated machine learning
- Describe capabilities of Azure Machine Learning
Topic 2: Features of Natural Language Processing (NLP) workloads on Azure15–20%- Describe capabilities of Azure Translator
- Identify types of NLP solutions
- Describe capabilities of Azure Speech
- Describe capabilities of Azure Language
Topic 3: Features of generative AI workloads on Azure20–25%- Describe use cases for generative AI
- Describe capabilities of Azure OpenAI Service
- Describe generative AI concepts
- Describe responsible AI practices for generative AI
Topic 4: Artificial Intelligence workloads and considerations15–20%- Describe responsible AI principles
- Describe considerations for developing AI solutions
- Identify types of AI workloads
Topic 5: Features of computer vision workloads on Azure15–20%- Describe capabilities of Azure Form Recognizer
- Describe capabilities of Azure Face
- Describe capabilities of Azure Computer Vision
- Identify types of computer vision solutions
- Describe capabilities of Azure Custom Vision

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