Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) : Professional-Data-Engineer日本語

Professional-Data-Engineer日本語 real exams

Exam Code: Professional-Data-Engineer-JPN

Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

Updated: Aug 12, 2026

Q & A: 433 Questions and Answers

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Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

  • United State - 151,247 USD
  • India - 25,42,327 INR
  • England - 115,632 POUND
  • Europe - 135,347 EURO

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

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

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