Updated: Jul 29, 2026
No. of Questions: 433 Questions & Answers with Testing Engine
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| Certification Vendor: | Google Cloud |
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Real Exam Qty: | 40 - 50 |
| Exam Price: | USD 200 (plus tax where applicable) |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Japanese |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Exam Format: | Multiple choice, Multiple select |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer日本語 Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.
Reference: https://cloud.google.com/certification/data-engineer
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Building data pipelines
|
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