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  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 26, 2026
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  • Exam Code: Professional-Data-Engineer
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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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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

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

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

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems24%- Data ingestion and integration
  • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
    • 2. Streaming ingestion (Pub/Sub, Dataflow)
      - Data processing and transformation
      • 1. ETL/ELT pipeline design
        • 2. Using Dataproc, Dataflow, and BigQuery SQL
          Topic 2: Ensuring solution quality28%- Reliability and performance
          • 1. Monitoring pipelines and workloads
            • 2. Fault tolerance and recovery strategies
              - Security and governance
              • 1. IAM and access control in GCP
                • 2. Data governance and compliance
                  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. Online vs batch prediction
                            • 2. Model monitoring and drift detection
                              - ML pipeline integration
                              • 1. Vertex AI pipeline deployment
                                • 2. Feature engineering and feature stores

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