Google Professional-Data-Engineer certification exam is a comprehensive test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that assess the candidates' knowledge of various Google Cloud Platform services, including BigQuery, Cloud Spanner, Cloud Dataflow, and Cloud Dataproc. Additionally, the exam evaluates the candidates' ability to design data processing systems that are scalable, reliable, and secure.
Reference: https://cloud.google.com/certification/data-engineer
The Google Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, and candidates are given two hours to complete it. Professional-Data-Engineer exam covers a range of topics, including designing data processing systems, building and maintaining data structures and databases, data analysis, machine learning, and data visualization.
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Earning the Google Professional-Data-Engineer certification demonstrates that an individual has the skills and knowledge required to design and maintain robust data processing systems using Google Cloud Platform services. Google Certified Professional Data Engineer Exam certification is recognized globally and is highly valued by organizations that use Google Cloud Platform for their data processing needs. With the increasing demand for data engineering professionals in the industry, earning this certification can help individuals stand out and advance their careers in this field.
Google Professional-Data-Engineer exam is intended for professionals who have experience working with data technologies and who are looking to demonstrate their expertise in designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam is designed to test a candidate's knowledge of various data engineering tools and technologies, including Google Cloud Storage, Google BigQuery, and Google Cloud Dataflow, among others.
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Storing the data (~20% of the exam) | 20% | - Selecting storage systems
|
| Topic 2: Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
|
| Topic 4: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Topic 5: Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
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