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

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 16, 2026

Q & A: 433 Questions and Answers

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

Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

  • Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
  • Designing and building data processing systems on the Google Cloud Platform
  • Influencing unstructured data using ML APIs on Cloud Dataproc
  • Predicting machine models using TensorFlow and Cloud ML
  • Enable insights from streaming data

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

Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

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Conclusion

Don't wait and enroll in these training courses offered by the official vendor that will help you ace the Professional Data Engineer exam with a good score. Once you take this exam and earn the prestigious Professional Data Engineer certification, you will get a chance to obtain a high-paying job and an amazing opportunity to work with the experts. Don't waste your time on other tasks and start preparing for this exam today. The more you practice the more you will get closer to success as a data analyst or data engineer. Moreover, it will polish your skills throughout and allow you to efficiently in well-reputed companies.

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

SectionWeightObjectives
Topic 1: Designing data processing systems22%- Batch and streaming data processing design
  • 1. Latency, throughput, and consistency trade-offs
    • 2. Event-driven vs batch architectures
      - Data architecture and storage design
      • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
        • 2. Designing scalable and cost-effective data models
          Topic 2: Ensuring solution quality28%- Security and governance
          • 1. Data governance and compliance
            • 2. IAM and access control in GCP
              - Reliability and performance
              • 1. Monitoring pipelines and workloads
                • 2. Fault tolerance and recovery strategies
                  Topic 3: 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
                          Topic 4: 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

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