Microsoft Azure AI Fundamentals Study Guide (2026) - Pass on Your First Attempt
📋 2026 Edition  ·  Updated May 2026

Microsoft Azure AI Fundamentals
azure-ai-fundamentals-ai-900 Study Guide — Pass First Attempt

Complete exam coverage for the Microsoft Azure AI Fundamentals. Every domain, every key topic — structured so you study smart, not hard. Built around the official exam blueprint.

40
Questions
45 min
Duration
70
Passing score
5
Domains
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First-attempt pass rate
47K+
Candidates prepared
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Average rating
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Overall readiness (locked)
Describe Artificial Intelligence Workloads and Considerations
—
Describe Fundamental Principles of Machine Learning on Azure
—
Describe Features of Computer Vision Workloads on Azure
—
Describe Features of Natural Language Processing Workloads on Azure
—
Describe Features of Generative AI Workloads on Azure
—
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Exam at a Glance

Everything you need to know before you start

Key facts about the Microsoft Azure AI Fundamentals exam structure, format, and scoring.

🆔
azure-ai-fundamentals-ai-900
Exam code
📝
40 questions
Total questions
⏱
45 minutes
Duration
🎯
70
Passing score
📋
5 domains
Exam domains
📅
Valid 3 years
Certification validity
🌐
Online / In-person
Testing mode
🏆
Globally recognised
Credential type
ℹ️
Scoring method: Scaled scoring from 100–1000. Passing score is 700. Scores do not directly correspond to number of correct answers due to Microsoft's scaled scoring model. Unscored items may appear.. The exam may include unscored pilot questions — treat every question seriously.
Focus Areas

What should you study for the Microsoft Azure AI Fundamentals exam?

To pass the Microsoft Azure AI Fundamentals certification exam, you should focus on these core domains. The exam tests your ability to apply concepts in real-world scenarios — not just memorise definitions.

⚠️
Common mistake: Candidates memorise terminology but struggle with scenario-based questions. Focus on when to use what, not just what exists.
🔐
Describe Artificial Intelligence Workloads and Considerations (17%)
Covers features of common AI workloads and guiding principles for responsible AI including fairness, reliability, privacy, inclusiveness, transparency, and accountability.
🏗
Describe Fundamental Principles of Machine Learning on Azure (17%)
Covers core machine learning techniques including regression, classification, and clustering, as well as Azure Machine Learning capabilities for automated ML, model management, and deployment.
⚡
Describe Features of Computer Vision Workloads on Azure (17%)
Covers common computer vision solution types including image classification, object detection, OCR, and facial detection, and maps them to Azure AI Vision and Face services.
💰
Describe Features of Natural Language Processing Workloads on Azure (17%)
Covers NLP workload scenarios including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech, and translation, along with Azure AI Language and Speech services.
🔄
Describe Features of Generative AI Workloads on Azure (22%)
Covers generative AI models, common scenarios, responsible AI for generative AI, and Azure services including Azure AI Foundry, Azure OpenAI Service, and the Azure AI Foundry model catalog.
Full Syllabus

Microsoft Azure AI Fundamentals Exam Syllabus and Topics

The Microsoft Azure AI Fundamentals exam is divided into 5 domains. Each domain tests specific skills and contributes to your overall score. Click any domain to expand topics.

Describe Artificial Intelligence Workloads and Considerations
Covers features of common AI workloads and guiding principles for responsible AI including fairness, reliability, privacy, inclusiveness, transparency, and accountability.
17% ▼
Identify Features of Common AI Workloads
Computer vision workloads
Natural language processing workloads
Document processing workloads
Generative AI workloads
Identify Guiding Principles for Responsible AI
Fairness in AI
Reliability and safety
Privacy and security
Inclusiveness
Transparency
Accountability
~7 questions
7 marks
17% of exam weight
Describe Fundamental Principles of Machine Learning on Azure
Covers core machine learning techniques including regression, classification, and clustering, as well as Azure Machine Learning capabilities for automated ML, model management, and deployment.
17% ▼
Identify Common Machine Learning Techniques
Regression scenarios
Classification scenarios
Clustering scenarios
Deep learning features
Transformer architecture
Describe Core Machine Learning Concepts
Features and labels in datasets
Training vs validation datasets
Describe Azure Machine Learning Capabilities
Automated machine learning (AutoML)
Data and compute services
Model management and deployment in Azure ML
~7 questions
7 marks
17% of exam weight
Describe Features of Computer Vision Workloads on Azure
Covers common computer vision solution types including image classification, object detection, OCR, and facial detection, and maps them to Azure AI Vision and Face services.
17% ▼
Identify Common Types of Computer Vision Solutions
Image classification
Object detection
Optical character recognition (OCR)
Facial detection and facial analysis
Identify Azure Tools and Services for Computer Vision
Azure AI Vision service capabilities
Azure AI Face detection service capabilities
~7 questions
7 marks
17% of exam weight
Describe Features of Natural Language Processing Workloads on Azure
Covers NLP workload scenarios including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech, and translation, along with Azure AI Language and Speech services.
17% ▼
Identify Features of Common NLP Workload Scenarios
Key phrase extraction
Entity recognition
Sentiment analysis
Language modeling
Speech recognition and synthesis
Translation
Identify Azure Tools and Services for NLP
Azure AI Language service capabilities
Azure AI Speech service capabilities
~7 questions
7 marks
17% of exam weight
Describe Features of Generative AI Workloads on Azure
Covers generative AI models, common scenarios, responsible AI for generative AI, and Azure services including Azure AI Foundry, Azure OpenAI Service, and the Azure AI Foundry model catalog.
22% ▼
Identify Features of Generative AI Solutions
Features of generative AI models (LLMs, multimodal)
Common generative AI scenarios (copilots, content generation, code generation)
Responsible AI for generative AI (grounding, content filtering, transparency)
Identify Generative AI Services and Capabilities in Microsoft Azure
Azure AI Foundry features and capabilities
Azure OpenAI Service features and capabilities
Azure AI Foundry model catalog
~9 questions
9 marks
22% of exam weight
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Study Plan

Microsoft Azure AI Fundamentals Structured Study Roadmap

Designed for candidates studying 1-2 hours per day. Select your timeline below.

Exam Strategy

Tips to pass Microsoft Azure AI Fundamentals on your first attempt

Tactical advice beyond content knowledge — what separates candidates who pass from those who retake.

🗓
The Generative AI domain (20–25%) is the highest-weighted section — prioritize Azure OpenAI Service, Azure AI Foundry, and responsible AI for generative models.
🔍
Use Microsoft Learn's free AI-900 learning paths and the official free Practice Assessment before sitting the exam.
⏭
Understand the difference between Azure AI Vision, Azure AI Language, Azure AI Speech, and Azure AI Foundry — questions frequently test which service applies to which scenario.
📊
Know the six principles of responsible AI: fairness, reliability, privacy, inclusiveness, transparency, and accountability.
🔁
Learn the differences between supervised (classification, regression), unsupervised (clustering), and deep learning — conceptual understanding is tested, not code.
🧪
Azure AI Foundry and the model catalog are new additions as of May 2025 — make sure to study these thoroughly.
📝
No programming or cloud experience is required — focus on concepts and which Azure service maps to which AI workload.
🎯
Use the exam sandbox at aka.ms/examdemo to familiarize yourself with question formats before test day.
🗓
The exam is available in multiple languages — non-English speakers may request 30 additional minutes.
🔍
Focus on scenario-based questions: given a business need, which Azure AI service would you use?
Recommended Resources

Official and trusted study materials

Curated resources ranked by usefulness. Quality over quantity — focus on a small set of authoritative sources.

Official
Official Exam Guide
The authoritative blueprint. Know every objective before studying anything else.
→ Vendor website
Practice Tests
Edureify Practice Tests
Full-length Microsoft Azure AI Fundamentals simulations with detailed per-domain analysis and explanations.
→ Start free test
Video Course
Structured Video Course
Pick one highly-rated course and complete it end-to-end before switching resources.
With edureify Convert Tutorial to AI Tutor
Reference
Domain Cheat Sheets
One-page summaries for each Microsoft Azure AI Fundamentals domain — ideal for last-week revision.
→ Get free Cheat Sheet
Community
Study Groups & Forums
Reddit r/certifications and exam-specific Discord servers for peer support and tips.
→ Reddit / Discord
AI Tutor
Edureify AI Mentor
Get instant answers to Microsoft Azure AI Fundamentals concepts, domain-level weak-area coaching, and adaptive questions.
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⚠️
Avoid brain dumps. Sites selling "real exam questions" violate most vendor NDAs and are legally risky. Questions rotate regularly — brain dumps lead to overconfidence on outdated material and a higher retake rate.
Reviews

What candidates say after passing

★★★★★
"Passed Microsoft Azure AI Fundamentals on my first attempt after 5 weeks. The domain-level diagnostic showed me exactly where my gaps were — I stopped wasting time on topics I already knew."
Rahul S.
Solutions Architect, Bangalore
★★★★★
"The structured study plan kept me on track. I tried studying on my own for 3 months and failed. With Edureify's roadmap I passed in 6 weeks."
Priya M.
Cloud Engineer, Mumbai
★★★★★
"The AI mentor was like having a personal tutor available at 2am. Every concept I didn't understand was explained until I got it. Invaluable for the Describe Artificial Intelligence Workloads and Considerations domain."
David K.
DevOps Engineer, London
FAQ

Frequently asked questions about Microsoft Azure AI Fundamentals

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