
Exam DP-100: Azure Data Scientist Associate - Practice Tests
Description
Microsoft Certified: Azure Data Scientist Associate
Advance your preparation for the Microsoft Exam DP-100: Designing and Implementing a Data Science Solution on Azure with this professionally crafted mock exam course. Designed for aspiring Azure Data Scientists, machine learning engineers, and data professionals, this course features six comprehensive mock exams that closely emulate the format, difficulty, and subject matter of the official certification.
The DP-100 exam assesses your ability to apply data science and machine learning solutions using Azure Machine Learning. These mock exams provide in-depth coverage of the core exam domains, including designing and preparing a machine learning workspace, performing data preparation, developing and training models, automating model training and tuning, and deploying and managing predictive models in production environments.
Each question is accompanied by a thorough explanation that clarifies not only the correct answer but also the underlying concepts, best practices, and rationale. These detailed explanations help reinforce your understanding of Azure Machine Learning tools and services, pipeline automation, model interpretability, and responsible AI practices.
Whether you are preparing for your first attempt or refining your knowledge, this course provides the practice and insights needed to build confidence and ensure exam readiness. Equip yourself with the skills and understanding necessary to become a certified Azure Data Scientist Associate and drive real-world machine learning projects in the cloud.
Can I retake the practice tests?
Yes, you can attempt each practice test as many times as you like. After completing a test, you'll see your final score. Each time you retake the test, the questions and answer choices will be shuffled for a fresh experience.
Is there a time limit for the practice tests?
Yes, each test includes a time limit of 120 seconds per question.
What score do I need to pass?
You need to score at least 70% on each practice test to pass.
Are explanations provided for the questions?
Yes, every question comes with a detailed explanation.
Can I review my answers after the test?
Absolutely. You’ll be able to review all your submitted answers and see which ones were correct or incorrect.
Are the questions updated frequently?
Yes, the questions are regularly updated to provide the best and most relevant learning experience.
Additional Note: It’s highly recommended that you take the practice exams multiple times until you're consistently scoring 90% or higher. Don’t hesitate—start your preparation today. Good luck!
Who this course is for:
- Data Scientists
- Machine Learning Engineers
- Experienced Data Science Practitioners
- AI and Data Professionals Transitioning to Azure
- Cloud Engineers and Solution Architects
- Academic and Research Professionals
- Technical Consultants and Service Providers
- Students and Graduates in Data-Focused Disciplines
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
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
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