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Google Professional-Data-Engineer日本語

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: Jul 28, 2026

Q&A Number: 433 Q&As

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

The following will be discussed here:

  • Transformation
  • Data cleansing
  • Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
  • Batch and streaming
  • Building and operationalizing storage systems
  • Building and operationalizing processing infrastructure
  • Testing and quality control
  • Validating a migration
  • Adjusting pipelines
  • Building and operationalizing data processing systems
  • Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
  • Storage costs and performance
  • Integrating with new data sources
  • Building and operationalizing pipelines
  • Monitoring pipelines
  • Provisioning resources
  • Lifecycle management of data
  • Data acquisition and import
  • Awareness of current state and how to migrate a design to a future state

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

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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

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

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