Practice Exams | Microsoft Azure AI-900 | Azure AI Fundament

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B"  last time you went through the test.NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.This exam is intended for you if you have both technical and non-technical backgrounds. Data science and software engineering experience are not required. However, you would benefit from having awareness of:Basic cloud conceptsClient-server applicationsYou can use Azure AI Fundamentals 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 at a glanceDescribe Artificial Intelligence workloads and considerations (15–20%)Describe fundamental principles of machine learning on Azure (20–25%)Describe features of computer vision workloads on Azure (15–20%)Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)Describe features of generative AI workloads on Azure (15–20%)Describe Artificial Intelligence workloads and considerations (15–20%)Identify features of common AI 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 compute services for data science and machine learningDescribe model management and deployment capabilities in Azure Machine LearningDescribe features of computer vision workloads on Azure (15–20%)Identify common types of computer vision solution:Identify features of image classification solutionsIdentify features of object detection solutionsIdentify features of optical character recognition solutionsIdentify features of facial detection and facial analysis solutionsIdentify Azure tools and services for computer vision tasksDescribe capabilities of the Azure AI Vision serviceDescribe capabilities of the Azure AI Face detection serviceDescribe capabilities of the Azure AI Video Indexer serviceDescribe features of Natural Language Processing (NLP) workloads on Azure (15–20%)Identify features of common NLP Workload ScenariosIdentify features and uses for key phrase extractionIdentify features and uses for entity recognitionIdentify features and uses for sentiment analysisIdentify features and uses for language modelingIdentify features and uses for speech recognition and synthesisIdentify features and uses for translationIdentify Azure tools and services for NLP workloadsDescribe capabilities of the Azure AI Language serviceDescribe capabilities of the Azure AI Speech serviceDescribe capabilities of the Azure AI Translator serviceDescribe features of generative AI workloads on Azure (15–20%)Identify features of generative AI solutionsIdentify features of generative AI modelsIdentify common scenarios for generative AIIdentify responsible AI considerations for generative AIIdentify capabilities of Azure OpenAI ServiceDescribe natural language generation capabilities of Azure OpenAI ServiceDescribe code generation capabilities of Azure OpenAI ServiceDescribe image generation capabilities of Azure OpenAI Service