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Exam Details
The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.
How to Prepare For Google Professional Data Engineer Exam
Preparation Guide for Google Professional Data Engineer Exam
Introduction to Google Professional Data Engineer Exam
Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.
The Professional Data Engineer exam assesses your ability to:
- Operationalize machine learning models
- Design data processing systems
- Build and operationalize data processing systems
- Ensure solution quality
Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Ensuring solution quality | 28% | - Security and governance
|
| Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
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