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Last Updated: Sep 13, 2026

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The Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is highly respected in the industry and is recognized by many organizations as a mark of excellence in data engineering. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is designed to help data professionals enhance their career prospects and gain recognition for their expertise in the field. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is also designed to provide organizations with a reliable way to identify qualified data professionals who can help them design and manage data processing systems on the Google Cloud Platform.

The Professional-Data-Engineer certification is designed for data professionals who are interested in working with large-scale data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is ideal for individuals who have experience working with data processing systems and are looking to advance their careers. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is also suitable for individuals who are interested in starting a career in data engineering and want to gain the skills and knowledge necessary to succeed in this field.

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

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

SectionWeightObjectives
Topic 1: Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cost optimization
  • 3. Cluster sizing and autoscaling
- Designing data pipelines
  • 1. Batch processing
  • 2. AI data enrichment
  • 3. Processing logic
  • 4. Data acquisition and import
  • 5. Streaming (e.g., windowing, late arriving data)
  • 6. Integrating with new data sources
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
Topic 2: Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Auditing, privacy, and compliance
  • 3. Data encryption
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
Topic 3: Storing the data (~20% of the exam)20%- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage
- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Designing the data model
  • 3. Deciding the degree of data normalization
  • 4. Defining architecture to support data access patterns
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Lifecycle management of data
  • 3. Analyzing data access patterns
Topic 4: Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Workflow orchestration
  • 2. Continuous integration and continuous deployment (CI/CD)
  • 3. Scheduling jobs
- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Planning for monitoring and alerting
  • 3. Performing data quality and validation checks
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
Topic 5: Preparing and using data for analysis (~15% of the exam)15%- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
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