Microsoft Azure AI Fundamentals Study Guide (2026)

Microsoft Azure AI Fundamentals Study Guide 2026 – Pass on Your First Attempt

This Microsoft Azure AI Fundamentals study guide covers all exam domains, key concepts, and real exam-style scenarios to help you pass on your first attempt. Learn what topics matter most, avoid common mistakes, and follow a structured plan based on the official exam blueprint.

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

To pass the Microsoft Azure AI Fundamentals certification exam, you should focus on:

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

The exam tests your ability to apply concepts in real scenarios, not just memorize definitions.

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.

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%
Weight
7
Questions
7
Marks

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

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%
Weight
7
Questions
7
Marks

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

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%
Weight
7
Questions
7
Marks

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

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%
Weight
7
Questions
7
Marks

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

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%
Weight
9
Questions
9
Marks

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
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Describe Artificial Intelligence Workloads and Considerations 85%
Describe Fundamental Principles of Machine Learning on Azure 92%

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95%
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