• Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Certification Provider: Google
  • Corresponding Certification:Google Cloud Certified
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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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

Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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)
          Designing data processing systems22%- Batch and streaming data processing design
          • 1. Event-driven vs batch architectures
            • 2. Latency, throughput, and consistency trade-offs
              - Data architecture and storage design
              • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                • 2. Designing scalable and cost-effective data models
                  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
                          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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