Azure Fundamentals training

Soon I will work on a few more certifications on Azure

As supplement to AZ-900 Azure Fundamentals, I will also complete DP-900 Azure Data Fundamentals and AI-900 Azure AI Fundamentals. This will be where I keep initial notes

Outline

DP-900 Describe core data concepts (25–30%) Identify considerations for relational data on Azure (20–25%) Describe considerations for working with non-relational data on Azure (15–20%) Describe an analytics workload (25–30%)

On eLearning modules take 5h59m without labs

AI-900

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

Identify features of common AI workloads Identify computer vision workloads Identify natural language processing workloads Identify document processing 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 (15–20%)

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 Identify features of the Transformer architecture

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 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 features of generative AI workloads on Azure (20–25%)

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 generative AI services and capabilities in Microsoft Azure Describe features and capabilities of Azure AI Foundry Describe features and capabilities of Azure OpenAI service Describe features and capabilities of Azure AI Foundry model catalog

On eLearning modules take 9h25m without labs