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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

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

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

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

SectionWeightObjectives
Building and operationalizing data processing systems24%- Data processing and transformation
  • 1. ETL/ELT pipeline design
    • 2. Using Dataproc, Dataflow, and BigQuery SQL
      - Data ingestion and integration
      • 1. Streaming ingestion (Pub/Sub, Dataflow)
        • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
          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
                  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
                          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

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