Exam AI-900: Microsoft Azure AI Fundamentals Study Guide 2024

The AI-900: Microsoft Azure AI Fundamentals exam is a crucial milestone for anyone looking to delve into the world of artificial intelligence and machine learning, especially within the Azure ecosystem. This exam serves as an essential foundation, covering a broad range of AI concepts and Azure services. It encompasses critical topics like AI workloads, machine learning principles, computer vision, natural language processing, and the emerging field of generative AI. These skills are not only pivotal for current technological trends but also hold immense future potential as AI continues to evolve and integrate into various sectors. This study guide is an invaluable resource for students and professionals alike, whether they are preparing for the AI-900 exam or simply seeking to enhance their understanding of AI. It provides a comprehensive overview of the exam content, structured in an accessible and easy-to-follow format, making it an ideal tool for effective learning and exam preparation.

Important Updates

In November 23, this exam was updated to include skills related to Generative AI, including the Azure OpenAI Service. It gives us an opportunity to learn more about this emerging field of AI and how it can be used to solve real-world problems, while taking the opportunity to get certified on it and showcase our skills.

I recommend you have a look to the official exam page to get the latest information about the exam, including the latest updates - https://learn.microsoft.com/en-ie/credentials/certifications/resources/study-guides/ai-900#updates-to-the-exam

Percentage breakdown

• Describe Artificial Intelligence workloads and considerations (15–20%)
• Describe fundamental principles of machine learning on Azure (20–25%)
• Describe features of computer vision workloads on Azure (15–20%)
• Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
• Describe features of generative AI workloads on Azure (15–20%)

Describe Artificial Intelligence workloads and considerations (15–20%)

• Identify features of common AI workloads

  • Identify features of data monitoring and anomaly detection workloads
  • Identify features of content moderation and personalization workloads
  • Identify computer vision workloads
  • Identify natural language processing workloads
  • Identify knowledge mining workloads
  • Identify document intelligence workloads
  • Identify features of generative AI workloads

• Identify guiding principles for responsible AI

  • Describe considerations for fairness in an AI solution
  • Describe considerations for reliability and safety in an AI solution
  • Describe considerations for privacy and security in an AI solution
  • Describe considerations for inclusiveness in an AI solution
  • Describe considerations for transparency in an AI solution
  • Describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (20–25%)

• Identify common machine learning techniques
  • Identify regression machine learning scenarios
  • Identify classification machine learning scenarios
  • Identify clustering machine learning scenarios
  • Identify features of deep learning techniques
• Describe core machine learning concepts
  • Identify features and labels in a dataset for machine learning
  • Describe how training and validation datasets are used in machine learning
• Describe Azure Machine Learning capabilities
  • Describe capabilities of Automated machine learning
  • Describe data and compute services for data science and machine learning
  • Describe model management and deployment capabilities in Azure Machine Learning

Describe features of computer vision workloads on Azure (15–20%)

• Identify common types of computer vision solution
  • Identify features of image classification solutions
  • Identify features of object detection solutions
  • Identify features of optical character recognition solutions
  • Identify features of facial detection and facial analysis solutions
• Identify Azure tools and services for computer vision tasks
  • Describe capabilities of the Azure AI Vision service
  • Describe capabilities of the Azure AI Face detection service
  • Describe capabilities of the Azure AI Video Indexer service

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

• Identify features of common NLP Workload Scenarios
  • Identify features and uses for key phrase extraction
  • Identify features and uses for entity recognition
  • Identify features and uses for sentiment analysis
  • Identify features and uses for language modeling
  • Identify features and uses for speech recognition and synthesis
  • Identify features and uses for translation
• Identify Azure tools and services for NLP workloads
  • Describe capabilities of the Azure AI Language service
  • Describe capabilities of the Azure AI Speech service
  • Describe capabilities of the Azure AI Translator service

Describe features of generative AI workloads on Azure (15–20%)

• Identify features of generative AI solutions
  • Identify features of generative AI models
  • Identify common scenarios for generative AI
  • Identify responsible AI considerations for generative AI
• Identify capabilities of Azure OpenAI Service
  • Describe natural language generation capabilities of Azure OpenAI Service
  • Describe code generation capabilities of Azure OpenAI Service
  • Describe image generation capabilities of Azure OpenAI Service

Additional resources

Below follows the list of additional resources that you should consider and a quick note to the Microsoft Learn collection shared there. I tried to extend the learning paths you have available on the exam’s page with some extra modules that I consider relevant to the exam.

Best of Luck and share your results with the community once you get certified! 😊💪

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