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: Sep 21, 2026

Q & A: 433 Questions and Answers

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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

  • Developers responsible for managing big data transformation initiatives
  • Business analysts
  • Data analysts
  • Data architects
  • Data engineers
  • Data scientists

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

Google Professional-Data-Engineer certification is an excellent way for data processing professionals to demonstrate their knowledge and skills in designing and building data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification can help individuals advance their careers and open up new opportunities in the field of data engineering.

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

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

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