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"Google Certified Professional Data Engineer Exam", also known as Professional-Data-Engineer exam, is a Google Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 433 Q&As to your Professional-Data-Engineer Exam preparation. In the Professional-Data-Engineer exam resources, you will cover every field and category in Google Cloud Certified Certification helping to ready you for your successful Google Certification.
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Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
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This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.
Reference: https://cloud.google.com/certification/data-engineer
There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 2: Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Topic 4: Designing data processing systems | 20% | - Designing for business requirements
|
| Topic 5: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
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