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

  • 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 25, 2026
  • Q & A: 433 Questions and Answers

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How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

  • Step1: Visit the Google Cloud Webassessor Website
  • Step2: Sign in or sign up to your Google Cloud Webassessor account
  • Step3: Search for the exam name Google Professional Data Engineer
  • Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.

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

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Build & Operationalize Data Processing Systems
  • Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
  • Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
  • Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
SectionWeightObjectives
Building and operationalizing data processing systems24%- Data ingestion and integration
  • 1. Streaming ingestion (Pub/Sub, Dataflow)
    • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
      - Data processing and transformation
      • 1. ETL/ELT pipeline design
        • 2. Using Dataproc, Dataflow, and BigQuery SQL
          Ensuring solution quality28%- Reliability and performance
          • 1. Fault tolerance and recovery strategies
            • 2. Monitoring pipelines and workloads
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  Operationalizing machine learning models26%- Model deployment and monitoring
                  • 1. Online vs batch prediction
                    • 2. Model monitoring and drift detection
                      - ML pipeline integration
                      • 1. Feature engineering and feature stores
                        • 2. Vertex AI pipeline deployment
                          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. Designing scalable and cost-effective data models
                                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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