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Microsoft Certified: Azure AI Fundamentals AI-900
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Skills measured AI 900
Describe AI workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of conversational AI workloads on Azure
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Overview of AI
AI is the creation of software that imitates human behaviors and capabilities. Key elements include:
Machine learning - This is often the foundation for an AI system, and is the way we "teach" a computer model to make predictions and draw conclusions from data.
Anomaly detection - The capability to automatically detect errors or unusual activity in a system.
Computer vision - The capability of software to interpret the world visually through cameras, video, and images.
Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind.
Conversational AI - The capability of a software "agent" to participate in a conversation.
Azure Machine Learning
Machine Learning is the basis of most AI solutions.
Microsoft Azure offers the following: Azure Machine Learning Service - A cloud-based platform that allows you to create, manage and publish machine learning models. Azure Machine Learning offers the following capabilities and features:
Automated machine-learning: This feature allows non-experts to create machine learning models quickly from data.
Azure Machine Learning designer: An interface that allows for no-code creation of machine learning solutions.
Data and compute management: Professional data scientists can access cloud-based data storage and compute resources to run code for data experiments at scale.
Pipelines: Software engineers, data scientists, and IT operations professionals are able to create pipelines that can be used to manage model deployment, training, and management.
