Professional-Data-Engineer日本語 Q&As - in .pdf

Professional-Data-Engineer日本語 pdf
  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 31, 2026
  • Q & A: 433 Questions and Answers
  • PDF Price: $69.99
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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 31, 2026
  • Q & A: 433 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Jul 31, 2026
  • Q & A: 433 Questions and Answers
  • Software Price: $69.99
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Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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

The following will be discussed here:

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

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

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

SectionWeightObjectives
Topic 1: 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 2: 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 3: 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 4: Ensuring solution quality28%- Security and governance
                          • 1. Data governance and compliance
                            • 2. IAM and access control in GCP
                              - Reliability and performance
                              • 1. Fault tolerance and recovery strategies
                                • 2. Monitoring pipelines and workloads

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