AI-900 Real Exam Questions
Microsoft Azure AI Fundamentals. Everything you need to prepare, practice, and pass.
246
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26
Exam Domains
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Certification Overview
The exam covers foundational AI concepts, including the definition and considerations for AI workloads, fundamental machine learning principles on Azure, and specific AI capabilities. Key areas include Computer Vision, Natural Language Processing (NLP) with topics like conversational AI and OCR, and Generative AI, prominently featuring Azure OpenAI, all while emphasizing Responsible AI principles.
What This Certification Proves
This certification validates foundational knowledge in core Artificial Intelligence (AI) concepts and how they are implemented using Microsoft Azure services. It proves a basic understanding of AI workloads, machine learning principles, computer vision, natural language processing, and generative AI, serving as a crucial entry point for individuals pursuing a career in AI on Azure.
Who Should Take This Exam
Individuals new to AI or cloud AI, business users, technical students, and those in non-technical roles who need to understand fundamental AI concepts and Azure AI services. This exam is suitable for anyone looking to validate foundational AI knowledge without deep technical implementation experience.
Topic Breakdown
26 domains covering 104 questions
| Domain | Questions | Weight |
|---|---|---|
| Describe Features Of Computer Vision Workloads On Azure | 25 | 24% |
| Describe Features Of Conversational Ai Workloads On Azure | 19 | 18% |
| Describe Ai Workloads And Considerations | 17 | 16% |
| Describe Features Of Generative Ai Workloads On Azure | 6 | 6% |
| Describe Features Of Computer Vision Workloads | 4 | 4% |
| Describe Fundamental Principles Of Machine Learning | 4 | 4% |
| Describe Features Of Conversational Ai Workloads | 3 | 3% |
| Describe Responsible Ai Principles | 3 | 3% |
| Explore Computer Vision Workloads | 2 | 2% |
| Describe Fundamental Principles Of Responsible Ai | 2 | 2% |
| Describe Basic Principles Of Machine Learning | 2 | 2% |
| Conversational Ai | 2 | 2% |
| Explore Fundamental Principles Of Machine Learning | 2 | 2% |
| Understand Computer Vision Workloads | 1 | 1% |
| Describe Basic Principles Of Machine Learning On Azure | 1 | 1% |
| Describe Computer Vision Workloads | 1 | 1% |
| Describe Features Of Azure Machine Learning | 1 | 1% |
| Describe Features Of Generative Ai Workloads | 1 | 1% |
| Describe Generative Ai On Azure | 1 | 1% |
| Describe Principles Of Responsible Ai | 1 | 1% |
| Explore Conversational Ai Workloads | 1 | 1% |
| Explore Fundamental Principles Of Machine Learning On Azure | 1 | 1% |
| Explore Natural Language Processing | 1 | 1% |
| Identify Guiding Principles For Responsible Ai | 1 | 1% |
| N/A | 1 | 1% |
| Computer Vision | 1 | 1% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Describe Features Of Computer Vision Workloads On Azure
- Read Microsoft official documentation
- Complete 9 questions daily
Week 3
- Deep dive: Describe Features Of Conversational Ai Workloads On Azure
- Review weak areas from results
- Take 2 full-length exams
Week 4
- Review all flagged questions
- Timed exams to build stamina
- Final revision of key concepts
60 Days
Balanced Approach
Week 1-2
- Survey all exam domains
- Set up study environment
- Begin with foundational topics
Week 3-4
- Focus: Describe Features Of Computer Vision Workloads On Azure
- Focus: Describe Features Of Conversational Ai Workloads On Azure
- 5 questions daily
Week 5-6
- Focus: Describe Ai Workloads And Considerations
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 246 questions
- Identify and eliminate weak areas
- Take 3 full-length timed tests
90 Days
Comprehensive Study
Month 1
- Learn all exam domains at a comfortable pace
- Build strong foundational knowledge
- 3 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 246 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
AI-900-Specific Tips
- Familiarize yourself with the core concepts of AI workloads and their considerations, understanding their benefits and ethical implications.
- Grasp the fundamental principles of machine learning, focusing on common ML tasks and their applications rather than intricate algorithms.
- Explore the features of Computer Vision on Azure, identifying use cases for object detection, facial recognition, and Optical Character Recognition (OCR).
- Understand the core concepts of Natural Language Processing (NLP) workloads, including sentiment analysis, key phrase extraction, and conversational AI applications.
- Dedicate time to understanding Generative AI and Azure OpenAI, focusing on what these technologies can do and their primary use cases.
- Pay close attention to Responsible AI principles, as this is a crucial and recurring theme across various AI services and is explicitly listed as a top topic.
- Review the various Azure AI services (e.g., Azure AI Vision, Azure AI Language, Azure OpenAI Service) that implement these concepts to understand their practical application.
Relevant Career Roles
Sample Questions
Try 5 free questions from the AI-900 question bank
You are explaining generative AI workflows to a colleague. What is the first step of a generative AI workflow?
You are processing photos of runners in a race. You need to read the numbers on the runners' shirts to identity the runners in the photos. Which type of computer vision should you use?
You have a webchat bot that provides responses from a Language Service knowledge base. You need to ensure that the bot uses user feedback to improve the relevance of the responses over time. What should you use?
You are building an AI-based loan approval app. You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant. This is an example of which Microsoft responsible AI principle?
You have a bot that identifies the brand names of products in images of supermarket shelves. Which service does the bot use?
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