Microsoft Azure AI Fundamentals [AI-900] Exam Guide (2026)

Microsoft Azure AI Fundamentals (AI-900) - Complete Exam Guide

Introduction: Why Get Azure AI Fundamentals Certified?

The Microsoft Azure AI Fundamentals (AI-900) certification validates your foundational knowledge of artificial intelligence concepts and Azure AI services. This certification is perfect for business stakeholders, technical professionals, and anyone looking to understand how AI can solve real-world problems using Azure's AI capabilities.

Unlike deep technical AI/ML certifications, the AI-900 focuses on understanding what different AI services do, when to use them, and how to implement AI responsibly.

You'll learn about machine learning basics, computer vision, natural language processing, and generative AI- all through the lens of Azure services. This certification demonstrates that you can identify appropriate AI solutions and understand ethical AI principles.

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Exam Overview: What You're Getting Into

Exam Details at a Glance

• Exam Code: AI-900
• Duration: 60 minutes
• Number of Questions: 40-60 questions
• Question Format: Multiple choice, multiple response, true/false, matching, drag-and-drop
• Passing Score: 700 out of 1000 (approximately 70%)
• Cost: $99 USD (or local currency equivalent)
• Validity: Does not expire
• Delivery Method: Pearson VUE testing centers or online proctored exam
• Prerequisites: None- foundational certification
• Recommended Knowledge: Basic cloud concepts, familiarity with AI concepts

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What Makes This Exam Different

This exam tests your understanding of AI capabilities and Azure services, not your ability to code ML models or build deep learning networks. You need to understand concepts like classification vs. clustering, computer vision tasks, NLP capabilities, and generative AI features but you don't need to implement them from scratch.

The exam emphasizes Responsible AI principles throughout. You'll encounter questions about fairness, reliability, privacy, transparency, and accountability in AI systems. Microsoft places strong emphasis on ethical AI development.

Exam Domains: Breaking Down What's Tested

Domain 1: Describe AI Workloads and Considerations (15-20% of exam)

What This Domain Covers:

This domain tests your understanding of what AI is, common AI workloads, and responsible AI principles.

Key Topics You'll Encounter:

• Common AI Workloads: Computer vision, natural language processing, document intelligence, generative AI
• Machine Learning vs. AI: Understanding the relationship and differences
• Responsible AI Principles:
  • Fairness: AI systems should treat all people fairly, avoiding bias
  • Reliability and Safety: AI systems should perform reliably and safely
  • Privacy and Security: AI systems should be secure and respect privacy
  • Inclusiveness: AI systems should empower everyone and engage people
  • Transparency: AI systems should be understandable
  • Accountability: People should be accountable for AI systems

What Success Looks Like:

You should be able to identify different AI workload types, explain responsible AI principles, and understand ethical considerations in AI development.

Exam Question Style:

"A company is developing an AI system for loan approvals. They want to ensure the system doesn't discriminate based on race or gender. Which Responsible AI principle is most relevant?"

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Domain 2: Describe Fundamental Principles of Machine Learning on Azure (15-20% of exam)

What This Domain Covers:

This domain tests your understanding of basic machine learning concepts, model training, and Azure Machine Learning capabilities.

Key Topics You'll Encounter:

• ML Task Types:
  • Classification: Predicting categories (e.g., spam vs. not spam, disease present vs. absent)
  • Regression: Predicting numeric values (e.g., house prices, temperature)
  • Clustering: Grouping similar items (e.g., customer segmentation)
• Core ML Concepts:
  • Features and Labels: Features are input variables, labels are what you predict
  • Training and Validation: Splitting data to train and evaluate models
  • Overfitting: Model performs well on training data but poorly on new data
  • Model Evaluation: Accuracy, precision, recall, F1 score, RMSE, R-squared
• Azure Machine Learning:
  • Automated ML (AutoML): Automatically find the best model
  • Azure ML Workspace: Central place to manage ML assets
  • Compute Instances/Clusters: Resources for training models
  • Model Registry and Deployment: Managing and deploying models as endpoints

What Success Looks Like:

You should be able to distinguish between classification, regression, and clustering, understand how to train and evaluate models, and know Azure ML capabilities.

Exam Question Style:

"A retail company wants to predict how much revenue a new product will generate based on advertising spend and seasonal factors. Which type of machine learning task is this?"

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Domain 3: Describe Features of Computer Vision Workloads on Azure (15-20% of exam)

What This Domain Covers:

This domain focuses on computer vision capabilities and Azure AI Vision services.

Key Topics You'll Encounter:

• Computer Vision Tasks:
  • Image Classification: Categorizing entire images (e.g., cat, dog, car)
  • Object Detection: Identifying and locating multiple objects in images with bounding boxes
  • Semantic Segmentation: Classifying each pixel in an image
  • Instance Segmentation: Identifying individual objects and their precise shapes
  • Optical Character Recognition (OCR): Extracting text from images
  • Face Detection and Analysis: Finding faces and analyzing attributes (age, emotion, etc.)
• Azure AI Vision Service:
  • Image analysis and tagging
  • Object detection
  • OCR and Read API for text extraction
  • Spatial analysis
  • Custom Vision for custom models
• Azure AI Document Intelligence: Form processing, invoice understanding, document analysis
• Azure AI Face: Face detection, verification, identification

What Success Looks Like:

You should be able to identify which computer vision task fits different scenarios and know which Azure service to use for each task.

Exam Question Style:

"A company needs to automatically extract text from thousands of scanned documents including handwriting. Which Azure service capability should they use?"

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Domain 4: Describe Features of Natural Language Processing Workloads on Azure (15-20% of exam)

What This Domain Covers:

This domain tests your knowledge of NLP capabilities and Azure AI Language services.

Key Topics You'll Encounter:

• NLP Tasks:
  • Key Phrase Extraction: Identifying main topics in text
  • Entity Recognition: Finding and categorizing entities (people, places, organizations, dates)
  • PII Detection: Identifying personally identifiable information
  • Sentiment Analysis: Determining positive, negative, or neutral sentiment
  • Language Detection: Identifying which language text is written in
  • Text Summarization: Creating concise summaries of longer texts
  • Text Classification: Categorizing text into custom categories
  • Question Answering: Extracting answers from knowledge bases
• Azure AI Language:
  • Named Entity Recognition (NER)
  • Sentiment analysis
  • Key phrase extraction
  • Language detection
  • Conversational Language Understanding (CLU)
  • Question answering service
• Azure AI Speech:
  • Speech-to-text (speech recognition)
  • Text-to-speech (speech synthesis)
  • Speech translation
  • Speaker recognition
• Azure AI Translator: Text translation across 100+ languages

What Success Looks Like:

You should be able to match NLP capabilities to business scenarios and identify appropriate Azure services for language processing tasks.

Exam Question Style:

"A customer service application needs to automatically determine if customer feedback is positive, negative, or neutral. Which Azure AI Language feature should be used?"

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Domain 5: Describe Features of Generative AI Workloads on Azure (20-25% of exam)

What This Domain Covers:

This is the largest domain and focuses on generative AI concepts, Azure OpenAI Service, and Azure AI Foundry.

Key Topics You'll Encounter:

• Generative AI Concepts:
  • Large Language Models (LLMs): GPT-4, GPT-3.5, etc.
  • Text Generation: Creating human-like text
  • Code Generation: Writing code from natural language descriptions
  • Image Generation: Creating images from text descriptions (DALL-E)
  • Prompt Engineering: Crafting effective prompts for better outputs
  • Grounding: Using your own data to answer questions (RAG - Retrieval Augmented Generation)
• Azure OpenAI Service:
  • GPT models for text generation
  • DALL-E for image generation
  • Embeddings for semantic search
  • Content filters for safety
  • Responsible AI considerations
• Azure AI Foundry:
  • Hubs and Projects: Organization structure for AI projects
  • Model Catalog: Pre-trained models from Microsoft and partners
  • Prompt Flow: Visual tool for building generative AI applications
  • Evaluation and Monitoring: Testing and tracking AI application performance
  • Content Safety: Filtering harmful content
• Responsible AI for Generative AI:
  • Hallucinations (model making up information)
  • Bias in generated content
  • Harmful content filtering
  • Transparency about AI-generated content
  • Data privacy and security

What Success Looks Like:

You should understand generative AI capabilities, know when to use Azure OpenAI vs. other services, and understand responsible AI considerations specific to generative models.

Exam Question Style:

"A company wants to build a chatbot that answers questions about their product documentation. The answers must be based only on their own documents, not the general knowledge of the AI model. Which approach should they use?"

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The Must-Know Services: Your Priority List

Focus on these Azure AI services for the exam.

Critical Services (Master These)

Machine Learning:

• Azure Machine Learning: Workspace, AutoML, compute, endpoints, model registry
• Azure Machine Learning Studio: Web interface for building ML solutions

Computer Vision:

• Azure AI Vision: Image analysis, OCR, object detection, face detection
• Azure AI Custom Vision: Build custom image classification and object detection models
• Azure AI Document Intelligence: Form processing, invoice analysis, document understanding
• Azure AI Face: Face detection, verification, identification

Natural Language Processing:

• Azure AI Language: Sentiment analysis, key phrase extraction, entity recognition, language detection
• Conversational Language Understanding (CLU): Build custom NLP models
• Question Answering: Create knowledge bases for Q&A
• Azure AI Translator: Text translation
• Azure AI Speech: Speech-to-text, text-to-speech, speech translation

Generative AI:

• Azure OpenAI Service: GPT models, DALL-E, embeddings, content filters
• Azure AI Foundry: Hubs, projects, model catalog, prompt flow, evaluation

Important Concepts (Know These Well)

• Responsible AI Principles: Fairness, reliability, privacy, inclusiveness, transparency, accountability
• ML Model Evaluation: Confusion matrix, accuracy, precision, recall, F1 score
• Data Splitting: Training, validation, and test sets
• Overfitting and Underfitting: Model performance issues
• Prompt Engineering: System messages, few-shot learning, temperature settings
• Grounding and RAG: Using your own data with generative models

High-Frequency Exam Topics

These concepts appear repeatedly:

1. Responsible AI Principles - Applied to various scenarios
2. Classification vs. Regression vs. Clustering - Identifying the right ML task
3. Computer Vision Tasks - Image classification vs. object detection vs. OCR
4. NLP Capabilities - Which feature for which scenario
5. Azure OpenAI Service - GPT models, content filtering, grounding
6. Sentiment Analysis - Determining positive/negative/neutral sentiment
7. Entity Recognition - Extracting names, places, dates from text
8. Prompt Engineering - Crafting effective prompts
9. AutoML - Automated model training and selection
10. Content Safety - Filtering harmful content in generative AI

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Study Strategy: Your Path to Success

Phase 1: AI Fundamentals and Responsible AI (1 Week)

Start with understanding what AI is and ethical principles.

Focus Areas:

• Common AI workload types
• All six Responsible AI principles with examples
• Difference between AI, ML, and deep learning

Phase 2: Machine Learning Basics (1-2 Weeks)

Learn fundamental ML concepts and Azure ML capabilities.

Focus Areas:

• Classification, regression, clustering with examples
• Training and evaluation process
• Azure ML workspace and AutoML
• Model metrics and evaluation

Phase 3: Computer Vision (1-2 Weeks)

Understand different vision tasks and Azure services.

Focus Areas:

• Image classification vs. object detection vs. segmentation
• OCR and document processing
• Azure AI Vision capabilities
• Face detection and analysis

Phase 4: Natural Language Processing (1-2 Weeks)

Learn NLP tasks and Azure AI Language services.

Focus Areas:

• Sentiment analysis, entity recognition, key phrases
• Question answering and language detection
• Azure AI Speech capabilities
• Translation services

Phase 5: Generative AI (2-3 Weeks)

This is the largest and newest domain—spend extra time here.

Focus Areas:

• How LLMs work at a high level
• Azure OpenAI Service models and capabilities
• Prompt engineering techniques
• Grounding and RAG patterns
• Azure AI Foundry platform
• Content safety and responsible AI for generative models

Phase 6: Practice and Refinement (1-2 Weeks)

Use practice exams to identify weak areas.

Strategy:

• Take Practice Set 1 under exam conditions
• Analyze weak domains
• Review study materials for weak areas
• Take Practice Set 2 and measure improvement
• Repeat until consistently scoring 85%+

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Exam Day Strategy: Maximizing Your Performance

Before the Exam

• Review Responsible AI principles
• Review service capabilities and when to use each
• Get adequate rest
• Have ID ready and arrive early

During the Exam

• Scenario Questions: Identify the business requirement first
• Service Matching: Know which service does what
• Responsible AI: These questions appear throughout—know all six principles
• Eliminate Wrong Answers: Rule out obviously incorrect options
• Don't Overthink: Questions are conceptual, not deeply technical
• Time Management: 60 minutes is usually sufficient—review flagged questions

Common Traps to Avoid

• Confusing Similar Concepts: Classification vs. clustering, object detection vs. segmentation
• Service Confusion: Azure AI Language vs. Azure AI Translator, Vision vs. Custom Vision
• Responsible AI: Know all six principles and which applies when
• Overgeneralizing: Some services have specific limitations or use cases

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Resources for Success

Official Microsoft Resources

• Microsoft Learn: Free, comprehensive training for AI-900
• Azure Free Account: Try Azure AI services with free credits
• Azure AI Services Documentation: Official service docs
• Microsoft AI Blog: Latest AI updates and capabilities

CloudFluently Course Package Includes

• Comprehensive Study Notes: Clear explanations of AI concepts and Azure services
• 180 Practice Questions: Three full-length exams with detailed explanations
• Hands-On Projects: Explore Azure AI services with guided exercises
• Quick Reference Cheatsheets: Service comparisons, Responsible AI principles
• Flashcards: Active recall for key concepts

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After You Pass: What's Next?

Career Advancement

• Pursue Role-Based Certifications: Azure AI Engineer Associate, Azure Data Scientist Associate
• Explore Specialty Certifications: Applied Skills in AI and ML
• Build AI Projects: Use Azure AI services to solve real problems
• Join Communities: AI/ML user groups, forums, conferences

Continued Learning

• Deep Dive into GenAI: Learn advanced prompt engineering and RAG patterns
• Hands-On Practice: Build chatbots, vision apps, NLP solutions
• Stay Current: AI evolves rapidly—follow Azure AI updates
• Learn Complementary Skills: Python, data science, MLOps

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Final Thoughts: You're Ready for This

The Azure AI Fundamentals certification validates your understanding of AI concepts and Azure's AI capabilities. This certification is valuable whether you're a business decision-maker evaluating AI solutions or a technical professional starting your AI journey.

Focus on understanding what each service does, when to use it, and how to implement AI responsibly. The exam tests conceptual understanding, not coding ability. If you can match scenarios to appropriate services and understand Responsible AI principles, you're well-prepared.

The combination of study notes, practice exams, and hands-on exploration in this package gives you everything you need to pass confidently.

Good luck, future Microsoft Certified: Azure AI Fundamentals! 🤖☁️