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Updated: Sep 14, 2026

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Ensure Solution Quality

  • Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
  • Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
  • Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
  • Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;

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
Real Exam Qty:Approximately 50–60 multiple choice and multiple select questions
Available Languages:German, English, Japanese, Portuguese, French, Spanish
Exam Format:case study, multiple select, multiple choice
Exam Duration:120 minutes
Exam Price:$200 USD
Certificate Validity Period:2 years
Passing Score:Not publicly disclosed (scaled scoring system)
Related Certifications:Google Cloud Certified - Professional Machine Learning Engineer
Google Cloud Certified - Professional Data Analytics Engineer
Google Cloud Certified - Professional Cloud Architect
Google Cloud Certified - Associate Cloud Engineer
Recommended Training:Google Cloud Data Engineering Learning Path
Google Cloud Professional Data Engineer Course (Coursera)
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

For more info visit:

Google-provided tutorials Community-provided tutorials Google-Data-Engineer-Practice-Test

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

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