Microsoft Certified: Azure AI Fundamentals (AI-900) — Simulado e guia de estudo

Identify features of common AI workloads including computer vision, natural language processing, document processing, and generative AI workloads. Identify guiding principles for responsible AI including fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability considerations.

Identify common machine learning techniques including regression, classification, clustering, deep learning, and Transformer architecture. Describe core machine learning concepts including features, labels, training and validation datasets. Describe Azure Machine Learning capabilities including automated machine learning, data and compute services, and model management and deployment capabilities.

Identify common types of computer vision solutions including image classification, object detection, optical character recognition, and facial detection and analysis. Identify Azure tools and services for computer vision tasks including Azure AI Vision service and Azure AI Face detection service capabilities.

Identify features of common NLP workload scenarios including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition and synthesis, and translation. Identify Azure tools and services for NLP workloads including Azure AI Language service and Azure AI Speech service capabilities.

Identify features of generative AI solutions including generative AI models, common scenarios for generative AI, and responsible AI considerations for generative AI. Identify generative AI services and capabilities in Microsoft Azure including Azure AI Foundry features and capabilities, Azure OpenAI service features and capabilities, and Azure AI Foundry model catalog.