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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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

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

SectionWeightObjectives
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. Event-driven vs batch architectures
        • 2. Latency, throughput, and consistency trade-offs
          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
                  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
                          Building and operationalizing data processing systems24%- Data processing and transformation
                          • 1. Using Dataproc, Dataflow, and BigQuery SQL
                            • 2. ETL/ELT pipeline design
                              - Data ingestion and integration
                              • 1. Streaming ingestion (Pub/Sub, Dataflow)
                                • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)

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