Microsoft AI-900 Exam Practice Questions

About this course

The course will include the below exam concepts:Describe Artificial Intelligence workloads and considerations (15–20%)Identify features of common AI workloadsIdentify features of data monitoring and anomaly detection workloadsIdentify features of content moderation and personalization workloadsIdentify computer vision workloadsIdentify natural language processing workloadsIdentify knowledge mining workloadsIdentify document intelligence workloadsIdentify features of generative AI workloadsIdentify guiding principles for responsible AIDescribe considerations for fairness in an AI solutionDescribe considerations for reliability and safety in an AI solutionDescribe considerations for privacy and security in an AI solutionDescribe considerations for inclusiveness in an AI solutionDescribe considerations for transparency in an AI solutionDescribe considerations for accountability in an AI solutionDescribe fundamental principles of machine learning on Azure (20–25%)Identify common machine learning techniquesIdentify regression machine learning scenariosIdentify classification machine learning scenariosIdentify clustering machine learning scenariosIdentify features of deep learning techniquesDescribe core machine learning conceptsIdentify features and labels in a dataset for machine learningDescribe how training and validation datasets are used in machine learningDescribe Azure Machine Learning capabilitiesDescribe capabilities of Automated machine learningDescribe data and com

Course objectives

• prepare for the Microsoft AI-900 certification exam
• develop a foundational knowledge of artificial intelligence and machine learning
• explore responsible and ethical practices in AI solutions