Requirements
The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.
Google Professional-Data-Engineer日本語 Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Certificate Validity Period: | 2 years |
| Passing Score: | 700 / 1000 |
| Related Certifications: | Google Cloud Associate Cloud Engineer Google Cloud Professional Cloud Architect Google Cloud Professional Data Analyst |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple select, Multiple choice |
| Exam Price: | USD 200 (plus tax where applicable) |
| Real Exam Qty: | 40 - 50 |
| Available Languages: | English, Japanese |
| 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 |
Google Professional-Data-Engineer certification is a valuable asset for data professionals who are seeking to advance their career in the field of data engineering. It demonstrates that a candidate has the skills and knowledge required to design, build, and maintain data processing systems on Google Cloud Platform, which is a highly sought-after skill in today’s data-driven world.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Topic 2: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Topic 4: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 5: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
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