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Updated: Aug 12, 2026
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Google Professional Data Engineer Certified Professional salary
The average salary of a Google Professional Data Engineer Certified Expert in
- England - 115,632 POUND
- India - 25,42,327 INR
- Europe - 135,347 EURO
- United State - 151,247 USD
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 Number: | Professional-Data-Engineer |
| Available Languages: | English, Japanese |
| Real Exam Qty: | 50-60 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not officially published (estimated ~80%) |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Exam Price: | $200 USD |
| Exam Format: | Multiple-choice, Multiple-select |
| Sample Questions: | Google Professional-Data-Engineer日本語 Sample Questions |
| Exam Way: | Online (remote proctored) or at a testing center (Kryterion) |
| Pre Condition: | No mandatory prerequisites. Recommended: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Google Cloud Big Data & Machine Learning Fundamentals course
This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
- Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
- Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
- Hire BigQuery and Cloud SQL for interactive data analysis
- Create ML models using BigQuery ML, APIs, and AutoML.
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:
- Cloud Dataflow Streaming Features
- Prebuilt ML Models APIs for Unsaturated Data
- Performing Spark on Cloud Dataproc
- Introduction to Data Engineering
- Introduction to Processing Streaming Data
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Creating a Data Lake
- Advanced BigQuery Performance and Functionality
- Custom Model building Utilizing Cloud AutoML
- Building a Data Warehouse
- Big Data Analytics with Cloud Al Platform Notebook
- Bigtable Streaming Features and High-Throughput BigQuery
- Introduction to Building Batch Data Pipelines
- Custom Model building Using SQL in BigQuery ML
- Serverless Data Processing with Cloud Dataflow
- Serverless Messaging Using Cloud Sub/Pub
- Production ML Pipelines and use of Kubeflow
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.
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Topic 2: Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Topic 4: Maintaining and automating data workloads (~15% of the exam) | 15% | - Monitoring data pipelines and data processes
|
| Topic 5: Storing the data (~20% of the exam) | 20% | - Using a data lake
|
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