
Google Cloud Professional Data Engineer (PDE) Practice Exams
Description
Google Cloud Professional Data Engineer
This course is a dedicated learning resource aimed at individuals preparing for the Google Cloud Professional Data Engineer certification exam or those expecting to sit for job interviews requiring extensive knowledge of the Google Cloud Platform (GCP). The course comprises a series of exhaustive practice exams that simulate the format and complexity of questions in the actual certification exam, thereby allowing learners to gauge their comprehension of GCP and experience conditions similar to the actual exam.
Each practice exam traverses a wide range of GCP topics, starting from foundational principles such as designing and building data processing systems and operationalizing machine learning models, to more intricate aspects like ensuring solution quality and managing data security and compliance. The questions are designed to probe both theoretical understanding and practical proficiency in engineering data solutions using GCP.
Upon completion of each exam, learners receive detailed explanations and solutions for every question, which enhances their learning journey and reinforces critical concepts. The course is designed for learners to attempt each exam multiple times, which facilitates progress tracking and identification of areas needing more focus.
The "Google Cloud Professional Data Engineer (PDE) Practice Exams" course is a vital tool for anyone gearing up for a GCP certification exam or a job interview. It efficiently pinpoints areas of strength and those that need further study. A robust understanding of GCP and data engineering principles is recommended for optimal learning outcomes.
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 Engineers
- Cloud Engineers
- Data Architects
- Machine Learning Engineers
- Business Intelligence (BI) Developers
- Database Administrators
- DevOps and Platform Engineers
- Aspiring Cloud Data Engineers
- IT Professionals Pursuing Career Advancement
- Consultants and Solution Providers
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