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

  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 21, 2026
  • Q&As: 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

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

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

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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

SectionWeightObjectives
Operationalizing machine learning models26%- Model deployment and monitoring
  • 1. Model monitoring and drift detection
    • 2. Online vs batch prediction
      - ML pipeline integration
      • 1. Vertex AI pipeline deployment
        • 2. Feature engineering and feature stores
          Ensuring solution quality28%- Reliability and performance
          • 1. Monitoring pipelines and workloads
            • 2. Fault tolerance and recovery strategies
              - Security and governance
              • 1. Data governance and compliance
                • 2. IAM and access control in GCP
                  Designing data processing systems22%- Batch and streaming data processing design
                  • 1. Event-driven vs batch architectures
                    • 2. Latency, throughput, and consistency trade-offs
                      - Data architecture and storage design
                      • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                        • 2. Designing scalable and cost-effective data models
                          Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                            • 2. Streaming ingestion (Pub/Sub, Dataflow)
                              - Data processing and transformation
                              • 1. ETL/ELT pipeline design
                                • 2. Using Dataproc, Dataflow, and BigQuery SQL

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