
Exam AI-900: Microsoft Azure AI Fundamentals Practice Tests
Description
The course "Exam AI-900: Microsoft Azure AI Fundamentals Practice Tests" is a meticulously designed resource for individuals aspiring to gain the AI-900 certification exam conducted by Microsoft. This foundational certification aims to validate your understanding and expertise in the key concepts of AI, and their implementation in the Microsoft Azure ecosystem.
The course brings to you a comprehensive set of practice tests that mirror the format, style, and difficulty of the actual AI-900 exam. It delves into the fundamentals of AI workloads and considerations, principles of machine learning on Azure, computer vision workloads on Azure, natural language processing workloads on Azure, and conversational AI workloads on Azure. Through these practice tests, you will gain extensive exposure to the various facets of the exam.
Each question within the practice tests is followed by an in-depth explanation of the correct answer, providing you with a better understanding of the underlying concepts. This practice-oriented approach aims to reinforce your understanding of the key AI principles and prepare you for the exam. By enrolling in the "Exam AI-900: Microsoft Azure AI Fundamentals Practice Tests" course, you'll enhance your readiness for the actual certification exam, gaining a comprehensive understanding of Azure AI fundamentals in the process.
The scope of the exam:
Identify features of common AI workloads
Identify guiding principles for responsible AI
Identify common machine learning types
Describe core machine learning concepts
Describe capabilities of visual tools in Azure Machine Learning Studio
Identify common types of computer vision solution
Identify Azure tools and services for computer vision tasks
Identify features of common NLP Workload Scenarios
Identify Azure tools and services for NLP workloads
Identify considerations for conversational AI solutions on Azure
Is it possible to take the practice test more than once?
Certainly, you are allowed to attempt each practice test multiple times. Upon completion of the practice test, your final outcome will be displayed. With every attempt, the sequence of questions and answers will be randomized.
Is there a time restriction for the practice tests?
Indeed, each test comes with a time constraint of 120 seconds for each question.
What score is required?
The target achievement threshold for each practice test is to achieve at least 70% correct answers.
Do the questions have explanations?
Yes, all questions have explanations for each answer.
Am I granted access to my responses?
Absolutely, you have the opportunity to review all the answers you submitted and ascertain which ones were correct and which ones were not.
Are the questions updated regularly?
Indeed, the questions are routinely updated to ensure the best learning experience.
Additional Note: It is strongly recommended that you take these exams multiple times until you consistently score 90% or higher on each test. Take the challenge without hesitation and start your journey today. Good luck!
Who this course is for:
- IT professionals or developers who want to validate their knowledge and skills in artificial intelligence (AI) fundamentals on the Microsoft Azure platform and prepare for the AI-900 certification exam
- data scientists or machine learning practitioners who want to gain a foundational understanding of AI concepts and how they are applied in Microsoft Azure
- students or individuals studying computer science, data science, or related fields who want to familiarize themselves with AI technologies and their applications in Azure
- professionals in non-technical roles, such as business analysts or project managers, who want to understand the basics of AI and its integration with Azure services for effective decision-making
- recruiters or hiring managers who want to evaluate the AI-related knowledge and competency of job candidates applying for roles involving Microsoft Azure AI services
- educators or trainers who want to assess the understanding and progress of their students in Microsoft Azure AI fundamentals and guide them towards certification
Instructor
EN
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
PL
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
Założyciel platformy e-smartdata
IG: e_smartdata