AI-900 Azure AI Fundamentals
Articles covering Microsoft AI-900 Azure AI Fundamentals exam objectives. These guides focus on AI workloads, computer vision, natural language processing, document processing, and generative AI topics essential for your Azure AI Fundamentals certification.
AI-900 Objective 1.1: Identify Features of Common AI Workloads
Comprehensive guide to understanding computer vision, natural language processing, document processing, and generative AI workloads for Microsoft AI-900 certification exam preparation.
AI-900 Objective 1.2: Identify Guiding Principles for Responsible AI
Complete guide to the six fundamental principles of responsible AI: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability for AI-900 certification.
AI-900 Objective 2.1: Identify Common Machine Learning Techniques
Comprehensive guide to machine learning techniques including regression, classification, clustering, deep learning, and Transformer architecture for AI-900 certification exam preparation.
AI-900 Objective 2.2: Describe Core Machine Learning Concepts
Complete guide to core machine learning concepts including features, labels, training and validation datasets, data splitting strategies, and avoiding common pitfalls for AI-900 certification.
AI-900 Objective 2.3: Describe Azure Machine Learning Capabilities
Comprehensive guide to Azure Machine Learning capabilities including AutoML, data and compute services, model management, and deployment features for AI-900 certification exam preparation.
AI-900 Objective 3.1: Identify Common Types of Computer Vision Solutions
Complete guide to computer vision solutions including image classification, object detection, OCR, and facial detection/analysis with their key features and applications for AI-900 certification.
AI-900 Objective 3.2: Identify Azure Tools and Services for Computer Vision Tasks
Comprehensive guide to Azure computer vision services including Azure AI Vision and Azure AI Face detection services with their capabilities and applications for AI-900 certification.
AI-900 Objective 4.1: Identify Features of Common NLP Workload Scenarios
Complete guide to NLP workload scenarios including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition/synthesis, and translation for AI-900 certification.
AI-900 Objective 4.2: Identify Azure Tools and Services for NLP Workloads
Comprehensive guide to Azure AI Language service and Azure AI Speech service capabilities for NLP workloads including sentiment analysis, entity recognition, speech-to-text, and text-to-speech for AI-900 certification.
AI-900 Objective 5.1: Identify Features of Generative AI Solutions
Complete guide to generative AI solutions including model features, common scenarios, and responsible AI considerations for content creation, software development, education, and healthcare applications for AI-900 certification.
AI-900 Objective 5.2: Identify Generative AI Services and Capabilities in Microsoft Azure
Comprehensive guide to Azure generative AI services including Azure AI Foundry, Azure OpenAI service, and Azure AI Foundry model catalog with their features, capabilities, and applications for AI-900 certification.