Professional-Data-Engineer日本語 exam dumps

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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • No. of Questions: 433 Questions and Answers
  • Updated: Jul 30, 2026

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

  • Building data processing systems
  • Designing data processing systems
  • Operationalizing machine learning models
  • Providing solution quality

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

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Build & Operationalize Data Processing Systems

  • 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;
  • Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.

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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

Training Courses Recommended for the Exam Preparation

Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.

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

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Reliability and performance
  • 1. Fault tolerance and recovery strategies
    • 2. Monitoring pipelines and workloads
      - Security and governance
      • 1. Data governance and compliance
        • 2. IAM and access control in GCP
          Topic 2: 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
                  Topic 3: 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
                          Topic 4: 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. Designing scalable and cost-effective data models
                                • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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