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Updated: Jul 28, 2026
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Building & Operationalizing Data Processing Systems
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.
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 |
| Passing Score: | Not officially published (estimated ~80%) |
| Exam Format: | Multiple-choice, Multiple-select |
| Available Languages: | English, Japanese |
| Exam Duration: | 120 minutes |
| Related Certifications: | Google Cloud Certified Professional Data Engineer |
| Real Exam Qty: | 50-60 |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| 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 |
This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:
- Additional Resources
- Introducing Google Cloud Platform ‘
- Data Processing Architectures
- Scaling Data Analytics
- Compute and Storage Fundamentals
- Machine Learning
- Data Analytics on the Cloud
Who is the Professional Data Engineer Exam Intended for?
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.
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:
- Create ML models using BigQuery ML, APIs, and AutoML.
- 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
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
| Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
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