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Last Updated: Aug 13, 2026

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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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

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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Security and governance
  • 1. Data governance and compliance
    • 2. IAM and access control in GCP
      - Reliability and performance
      • 1. Fault tolerance and recovery strategies
        • 2. Monitoring pipelines and workloads
          Topic 2: 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)
                  Topic 3: Designing data processing systems22%- Data architecture and storage design
                  • 1. Designing scalable and cost-effective data models
                    • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                      - Batch and streaming data processing design
                      • 1. Event-driven vs batch architectures
                        • 2. Latency, throughput, and consistency trade-offs
                          Topic 4: Operationalizing machine learning models26%- Model deployment and monitoring
                          • 1. Model monitoring and drift detection
                            • 2. Online vs batch prediction
                              - ML pipeline integration
                              • 1. Feature engineering and feature stores
                                • 2. Vertex AI pipeline deployment
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