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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions
The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
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The benefit of obtaining the Google Professional Data Engineer Exam Certification
A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
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Introduction
Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ingesting and processing the data (~20% of the exam) | 20% | - Performing security considerations
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
| Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
|
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