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Updated: Sep 22, 2026
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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:
- Bigtable Streaming Features and High-Throughput BigQuery
- Custom Model building Using SQL in BigQuery ML
- Advanced BigQuery Performance and Functionality
- Building a Data Warehouse
- Serverless Data Processing with Cloud Dataflow
- Prebuilt ML Models APIs for Unsaturated Data
- Custom Model building Utilizing Cloud AutoML
- Cloud Dataflow Streaming Features
- Introduction to Processing Streaming Data
- Creating a Data Lake
- Performing Spark on Cloud Dataproc
- Introduction to Building Batch Data Pipelines
- Introduction to Data Engineering
- Big Data Analytics with Cloud Al Platform Notebook
- Serverless Messaging Using Cloud Sub/Pub
- Production ML Pipelines and use of Kubeflow
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Exam Price: | $200 USD |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Related Certifications: | Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Machine Learning Engineer Google Cloud Certified - Associate Cloud Engineer |
| Exam Format: | case study, multiple select, multiple choice |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Exam Duration: | 120 minutes |
| Available Languages: | French, Portuguese, Spanish, English, German, Japanese |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Google Cloud Professional Data Engineer Course (Coursera) Google Cloud Data Engineering Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer日本語 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 |
Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions
The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 22% | - Data architecture and storage design
|
| Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Ensuring solution quality | 28% | - Security and governance
|
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