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To become a certified Google Professional Data Engineer, candidates must pass the Professional-Data-Engineer exam. Professional-Data-Engineer-JPN exam consists of multiple-choice and scenario-based questions that test the candidate's knowledge of Google Cloud data processing technologies, data storage solutions, and data analysis tools. Professional-Data-Engineer-JPN exam also evaluates the candidate's ability to design and implement data pipelines using Google Cloud services, such as BigQuery, Cloud Dataflow, and Cloud Dataproc.

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In order to be eligible for the exam, candidates must have at least three years of industry experience, with at least one year of experience working with Google Cloud Platform. Professional-Data-Engineer-JPN exam consists of multiple-choice and multiple-select questions and is designed to be completed within two hours. The passing score for the exam is 70%.

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