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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

  • Awareness of current state and how to migrate a design to a future state
  • Adjusting pipelines
  • Integrating with new data sources
  • Storage costs and performance
  • Building and operationalizing pipelines
  • Building and operationalizing data processing systems
  • Building and operationalizing storage systems
  • Batch and streaming
  • Lifecycle management of data
  • Transformation
  • Building and operationalizing processing infrastructure
  • Testing and quality control
  • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  • Monitoring pipelines
  • Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
  • Data cleansing
  • Validating a migration
  • Provisioning resources
  • Data acquisition and import

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Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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. Using Dataproc, Dataflow, and BigQuery SQL
        • 2. ETL/ELT pipeline design
          Ensuring solution quality28%- Security and governance
          • 1. IAM and access control in GCP
            • 2. Data governance and compliance
              - Reliability and performance
              • 1. Fault tolerance and recovery strategies
                • 2. Monitoring pipelines and workloads
                  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. Latency, throughput, and consistency trade-offs
                        • 2. Event-driven vs batch architectures
                          Operationalizing machine learning models26%- ML pipeline integration
                          • 1. Feature engineering and feature stores
                            • 2. Vertex AI pipeline deployment
                              - Model deployment and monitoring
                              • 1. Model monitoring and drift detection
                                • 2. Online vs batch prediction

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