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Updated: Sep 10, 2026

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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.

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

Google Professional-Data-Engineer日本語 Exam Overview:

Certification Vendor:Google Cloud
Exam Name:Google Cloud Certified Professional Data Engineer Exam
Exam Number:Professional Data Engineer
Exam Format:multiple choice, multiple select, case study
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Available Languages:Portuguese, Japanese, German, Spanish, French, English
Related Certifications:Google Cloud Certified - Professional Cloud Architect
Google Cloud Certified - Professional Machine Learning Engineer
Google Cloud Certified - Professional Data Analytics Engineer
Google Cloud Certified - Associate Cloud Engineer
Passing Score:Not publicly disclosed (scaled scoring system)
Certificate Validity Period:2 years
Exam Price:$200 USD
Exam Duration:120 minutes
Recommended Training:Google Cloud Professional Data Engineer Course (Coursera)
Google Cloud Data Engineering Learning Path
Exam Registration:Google Cloud Certification Registration
Sample Questions:Google Professional-Data-Engineer日本語 Sample Questions
Exam Way:Online proctored exam or onsite test center (Kryterion Webassessor)
Pre Condition:No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services
Official Syllabus URL:https://cloud.google.com/learn/certification/data-engineer

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

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

SectionWeightObjectives
Topic 1: Operationalizing machine learning models26%- Model deployment and monitoring
  • 1. Online vs batch prediction
    • 2. Model monitoring and drift detection
      - ML pipeline integration
      • 1. Feature engineering and feature stores
        • 2. Vertex AI pipeline deployment
          Topic 2: 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. ETL/ELT pipeline design
                • 2. Using Dataproc, Dataflow, and BigQuery SQL
                  Topic 3: 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
                          Topic 4: 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

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