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What is the duration, language, and format of Google Professional Data Engineer Exam
- Passing score: 80%
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Cost: $200
- Length of Examination: 120 minutes
- Number of Questions: 50-60
- Format: Multiple choices, multiple answers
Career Path
Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.
The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Designing data processing systems | 22% | - Data architecture and storage design
|
| Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
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