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  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Q & A: 433 Questions and Answers
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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. Data acquisition and import
  • 3. Integrating with new data sources
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. Processing logic
  • 6. AI data enrichment
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Topic 2: Storing the data (~20% of the exam)20%- 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
- Using a data lake
  • 1. Managing the lake (data discovery, access, cost controls)
  • 2. Processing data
  • 3. Monitoring the data lake
- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Mapping business requirements
  • 3. Designing the data model
  • 4. Deciding the degree of data normalization
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
Topic 3: Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Data encryption
  • 3. Identity and Access Management (IAM)
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
Topic 4: 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 5: Maintaining and automating data workloads (~15% of the exam)15%- 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. Workflow orchestration
  • 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

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