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Microsoft Azure AI Fundamentals (AI-900)

Exam Overview

The Azure AI Fundamentals certification validates foundational knowledge of machine learning and artificial intelligence concepts and related Microsoft Azure services. This exam is intended for candidates with both technical and non-technical backgrounds who want to demonstrate their knowledge of AI workloads and the ability to identify Azure services to support them.

Exam Details: - Exam Code: AI-900 - Duration: 60 minutes - Number of Questions: 40-60 questions - Passing Score: 700 out of 1000 - Question Types: Multiple choice, multiple select, drag and drop, hot area, case studies - Cost: $99 USD - Prerequisites: None (basic computer literacy recommended)

Exam Domains

1. Describe Artificial Intelligence Workloads and Considerations (15-20%)

  • Common AI Workloads
  • Machine learning workloads
  • Computer vision workloads
  • Natural language processing workloads
  • Knowledge mining workloads
  • Document intelligence workloads
  • Generative AI workloads
  • Guiding Principles for Responsible AI
  • Fairness, reliability and safety, privacy and security
  • Inclusiveness, transparency, accountability

2. Describe Fundamental Principles of Machine Learning on Azure (20-25%)

  • Common Machine Learning Techniques
  • Regression, classification, clustering
  • Deep learning
  • Core Machine Learning Concepts
  • Features and labels, training and validation datasets
  • Model evaluation metrics
  • Core Tasks in Creating ML Solutions
  • Data ingestion and preparation
  • Feature engineering and selection
  • Model training and evaluation
  • Model deployment and management
  • Capabilities of Azure Machine Learning
  • Automated ML, Azure Machine Learning designer
  • Data and compute management
  • Model management and deployment

3. Describe Features of Computer Vision Workloads on Azure (15-20%)

  • Common Types of Computer Vision Solutions
  • Image classification, object detection
  • Optical character recognition (OCR)
  • Facial detection, analysis, and recognition
  • Azure Tools and Services for Computer Vision
  • Azure AI Vision service
  • Azure AI Custom Vision
  • Azure AI Face service
  • Azure AI Video Indexer

4. Describe Features of Natural Language Processing (NLP) Workloads on Azure (15-20%)

  • Common NLP Workload Scenarios
  • Key phrase extraction, entity recognition
  • Sentiment analysis, language modeling
  • Speech recognition and synthesis
  • Translation
  • Azure Tools and Services for NLP
  • Azure AI Language service
  • Azure AI Speech service
  • Azure AI Translator service

5. Describe Features of Generative AI Workloads on Azure (15-20%)

  • Features of Generative AI Solutions
  • Natural language generation
  • Code generation
  • Image generation
  • Capabilities of Azure OpenAI Service
  • Natural language models (GPT-3.5, GPT-4)
  • Code generation models (Codex)
  • Image generation models (DALL-E)
  • Responsible AI Considerations for Generative AI
  • Content filtering and safety systems
  • Bias and fairness considerations

6. Describe Features of Knowledge Mining and Document Intelligence Workloads on Azure (10-15%)

  • Knowledge Mining Workloads
  • Azure AI Search capabilities
  • Index creation and management
  • Document Intelligence Workloads
  • Azure AI Document Intelligence
  • Pre-built and custom models
  • Form recognition capabilities

Study Tips

  1. Week 1: AI fundamentals and responsible AI
  2. Week 2-3: Machine learning concepts and Azure ML
  3. Week 4: Computer vision and NLP services
  4. Week 5-6: Generative AI, knowledge mining, and practice

Key Study Resources

  • Microsoft Learn Learning Paths:
  • Get started with artificial intelligence on Azure
  • Create machine learning models
  • Explore computer vision in Microsoft Azure
  • Explore natural language processing
  • Explore knowledge mining
  • Introduction to Azure OpenAI Service

Hands-on Practice

  • Create Azure AI services in the portal
  • Use Azure AI Vision for image analysis
  • Experiment with Azure AI Language service
  • Try Azure OpenAI Service (if available)
  • Build simple ML models with Azure ML designer
  • Explore Azure AI Search capabilities

Exam Strategy

  • Understand the difference between AI, ML, and deep learning
  • Know when to use different Azure AI services
  • Focus on capabilities rather than deep technical implementation
  • Understand responsible AI principles and their application
  • Practice identifying appropriate services for given scenarios

Common Gotchas

  • Distinguish between different computer vision tasks
  • Understand the various NLP capabilities and use cases
  • Know the different Azure AI service offerings
  • Understand responsible AI principles
  • Be familiar with generative AI capabilities and limitations

Comprehensive Study Resources

👉 Complete Azure Study Resources Guide

For detailed information on courses, practice tests, hands-on labs, communities, and more, see our comprehensive Azure study resources guide which includes: - Official Microsoft Learn paths (FREE) - Top-rated video courses with specific instructors - Practice test platforms with pricing and comparisons - Hands-on lab environments and free tier details - Community forums and study groups - Essential tools and Azure CLI resources - Pro tips and budget-friendly study strategies

Remember: This exam focuses on understanding AI concepts and knowing which Azure services to use for different AI scenarios. Hands-on experience with Azure AI services is highly recommended.