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Google Professional-Data-Engineer certification exam is designed for professionals seeking to demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, data architects, and data analysts who work with big data solutions. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and build data processing systems, as well as manage and monitor them in a production environment.
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To become a certified Google Professional Data Engineer, candidates must pass the Professional-Data-Engineer exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that test the candidate's knowledge of Google Cloud data processing technologies, data storage solutions, and data analysis tools. Professional-Data-Engineer exam also evaluates the candidate's ability to design and implement data pipelines using Google Cloud services, such as BigQuery, Cloud Dataflow, and Cloud Dataproc.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer certification has become a must-have for data engineering professionals who work with Google Cloud. Google Certified Professional Data Engineer Exam certification validates their knowledge and skills in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification also helps professionals to stand out in a competitive job market and advance their careers.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
| Topic 2: Designing data processing systems (~30% of the exam) | 30% | - Designing data pipelines
|
| Topic 3: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Topic 4: Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
| Topic 5: Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
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