Overview

Welcome to the ultimate practice exams course designed to give you the winning edge in your journey to becoming Microsoft Azure AI Fundamentals - AI-900 certified!Are you ready to pass the Microsoft Azure AI Fundamentals (AI-900) certification exam? Find out by testing yourself with this new offering on Udemy. Each of the 6 full practice tests in this set provides an entire exam's worth of questions, enabling you to confirm your mastery of the topics and providing you with the confidence you'll need to take your Microsoft Azure AI Fundamentals (AI-900) Certification exam.The tests in this set are timed, so you'll know when you're taking more time than the official test allows, and at the end of the test, you'll receive a personal breakdown of the questions you answered correctly and incorrectly to improve your knowledge and make you more prepared to pass the actual Microsoft exam.AI-900: Microsoft Azure AI Fundamentals Exam details:Exam Name: Microsoft Certified - Azure AI FundamentalsExam Code: AI-900Exam Price: $99 (USD)Number of Questions: Maximum of 40-60 questions,Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,Length of Test: 60 Minutes. The exam is available in English and Japanese languages.Passing Score: 700 / 1000Languages: English, Japanese, Korean, and Simplified ChineseSchedule Exam: Pearson VUEAI-900: Microsoft Azure AI Fundamentals Certification Exams skill questions:Skill Measurement Exam Topics:-Describe Artificial Intelligence workloads and considerations (20-25%)Describe fundamental principles of machine learning on Azure (25-30%)Describe features of computer vision workloads on Azure (15-20%)Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)##) Describe Artificial Intelligence workloads and considerations (20-25%)Identify features of common AI workloadsIdentify features of anomaly detection workloadsIdentify computer vision workloadsIdentify natural language processing workloadsIdentify knowledge mining 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 solution#) Describe fundamental principles of machine learning on Azure (25-30%)Identify common machine learning typesIdentify regression machine learning scenariosIdentify classification machine learning scenariosIdentify clustering machine learning scenariosDescribe core machine learning conceptsIdentify features and labels in a dataset for machine learningDescribe how training and validation datasets are used in machine learningDescribe capabilities of visual tools in Azure Machine Learning StudioAutomated machine learningAzure Machine Learning designer#) Describe features of computer vision workloads on Azure (15-20%)Identify common types of computer vision solutionIdentify 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 tasksIdentify capabilities of the Computer Vision serviceIdentify capabilities of the Custom Vision serviceIdentify capabilities of the Face serviceIdentify capabilities of the Form Recognizer service#) Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)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 workloadsIdentify capabilities of the Language serviceIdentify capabilities of the Speech serviceIdentify capabilities of the Translator serviceIdentify considerations for conversational AI solutions on AzureIdentify features and uses for botsIdentify capabilities of Power Virtual Agents and the Azure Bot serviceAzure 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 is not a prerequisite for any of them.You may be eligible for ACE college credit if you pass this certification exam. See ACE college credit for certification exams for details.Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; and features of Natural Language Processing (NLP) workloads on Azure.

Skills

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