Updated: Oct 08, 2026
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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Related Certifications: | Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Cloud Architect |
| Exam Format: | multiple choice, case study, multiple select |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Exam Price: | $200 USD |
| Exam Duration: | 120 minutes |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Certificate Validity Period: | 2 years |
| Available Languages: | Spanish, Japanese, French, Portuguese, German, English |
| Recommended Training: | Google Cloud Data Engineering Learning Path Google Cloud Professional Data Engineer Course (Coursera) |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Kryterion Webassessor) |
| Pre Condition: | No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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
The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Topic 2: Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Topic 3: Ensuring solution quality | 28% | - Security and governance
|
| Topic 4: Designing data processing systems | 22% | - Data architecture and storage design
|
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