Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) : Professional-Data-Engineer日本語 exam

Professional-Data-Engineer日本語
  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jun 14, 2026
  • Q & A: 403 Questions and Answers

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

  • Mapping to current and future business requirements
  • Building and running test suites
  • Assessing, troubleshooting, and improving data representations and data processing infrastructure
  • Ensuring scalability and efficiency
  • Identity and access management (e.g.,Cloud IAM)
  • Choosing between ACID, idempotent, eventually consistent requirements
  • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
  • Resizing and autoscaling resources
  • Data staging, cataloging, and discovery
  • Designing for security and compliance
  • Ensuring flexibility and portability
  • Pipeline monitoring (e.g., Stackdriver)
  • Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
  • Ensuring privacy (e.g., Data Loss Prevention API)
  • Data security (encryption, key management)
  • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  • Verification and monitoring
  • Ensuring reliability and fidelity
  • Performing data preparation and quality control (e.g., Cloud Dataprep)

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

The following will be discussed here:

  • Selecting the appropriate storage technologies
  • Data modeling
  • At least once, in-order, and exactly once, etc., event processing
  • Distributed systems
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • Designing data pipelines
  • Designing data processing systems
  • Schema design
  • System availability and fault tolerance
  • Choice of infrastructure
  • Data publishing and visualization (e.g., BigQuery)
  • Online (interactive) vs. batch predictions
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • Tradeoffs involving latency, throughput, transactions
  • Hybrid cloud and edge computing
  • Job automation and orchestration (e.g., Cloud Composer)
  • Use of distributed systems
  • Capacity planning
  • Mapping storage systems to business requirements

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

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