This practice test course contains 5 complete timed AI-900 practice tests. That’s 250+ unique questions to test how well prepared you are for the real exam. This practice test course is designed to cover every topic, with a difficulty level like a real exam. Every question has a detailed answer with the links back to the official Microsoft docs. Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are 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 its not a prerequisite for any of them. Skills measuredDescribe AI workloads and considerationsDescribe fundamental principles of machine learning on AzureDescribe features of computer vision workloads on AzureDescribe features of Natural Language Processing (NLP) workloads on AzureDescribe features of conversational AI workloads on AzureKEY FEATURES OF THESE POPULAR PRACTICE EXAMS258 PRACTICE QUESTIONS: 5 sets of Practice Examsand 3 case study available on Udemy to assess your exam readiness. EXAM SIMULATION: All Microsoft azure AI-900 Practice Tests are timed and scored (passing score is 70%) mimicking the real exam environmentDETAILED EXPLANATIONS: Every question includes a detailed explanation that explains why each answer is correct or incorrectPREMIUM-QUALITY: These practice questions are free from typos and technical errors which makes your learning experience much more pleasantALWAYS UP TO DATE: Our question bank is constantly updated based on student feedback from the real exam. New questions are added on a regular basis growing our pool of questionsACTIVE Q & A FORUM: In this discussion board, students ask questions and share their recent exam experience offering feedback on which topics were covered. RESPONSIVE SUPPORT: Our team of Azure experts respond to all of your questions, concerns or feedback. Each question has detailed explanations at the end of each set that will help you gain a deeper understanding of the Azure services. MOBILE-COMPATIBLE - so you can conveniently review everywhere, anytime with your smartphone! Plus a 30 DAY MONEY BACK GUARANTEE if you’re not satisfied for any reason. NOTE: The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive. NOTE: In most cases, exams do NOT cover preview features, and some features will only be added to an exam when they are GA (General Availability).Describe Artificial Intelligence workloads and considerations (15-20%) Identify features of common AI workloads identify prediction/forecasting workloads identify features of anomaly detection workloads identify computer vision workloads identify natural language processing or knowledge mining workloads identify conversational AI workloadsIdentify 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 (30- 35%) Identify common machine learning types identify regression machine learning scenarios identify classification machine learning scenarios identify clustering machine learning scenarios Describe core machine learning conceptsidentify features and labels in a dataset for machine learning describe how training and validation datasets are used in machine learning describe how machine learning algorithms are used for model training select and interpret model evaluation metrics for classification and regression Identify core tasks in creating a machine learning solution describe common features of data ingestion and preparation describe feature engineering and selection describe common features of model training and evaluation describe common features of model deployment and management Describe capabilities of no-code machine learning with Azure Machine Learning studio automated ML UI AI-900 azure Machine Learning designer 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 featur
