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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 29, 2026
  • Q & A: 433 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 29, 2026
  • Q & A: 433 Questions and Answers
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Three versions of Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) exam questions provided

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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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
  • Europe - 135,347 EURO
  • India - 25,42,327 INR
  • United State - 151,247 USD

Reference: https://cloud.google.com/certification/data-engineer

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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:

  • Introducing Google Cloud Platform ‘
  • Compute and Storage Fundamentals
  • Data Processing Architectures
  • Data Analytics on the Cloud
  • Scaling Data Analytics
  • Machine Learning
  • Additional Resources

Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Cluster sizing and autoscaling
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cost optimization
- Designing data pipelines
  • 1. Batch processing
  • 2. AI data enrichment
  • 3. Processing logic
  • 4. Data acquisition and import
  • 5. Integrating with new data sources
  • 6. Streaming (e.g., windowing, late arriving data)
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Designing the data model
  • 3. Defining architecture to support data access patterns
  • 4. Deciding the degree of data normalization
- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Lifecycle management of data
  • 3. Analyzing data access patterns
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Processing data
  • 3. Managing the lake (data discovery, access, cost controls)
Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Planning for monitoring and alerting
  • 2. Performing data quality and validation checks
  • 3. Recovering from failures
- Automating data processes
  • 1. Scheduling jobs
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Workflow orchestration
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting

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