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The Google Professional-Data-Engineer-JPN exam for the Google Professional-Data-Engineer certification is a comprehensive and challenging test that covers a wide range of topics related to data engineering. Professional-Data-Engineer-JPN exam consists of multiple-choice and scenario-based questions, which require candidates to apply their knowledge to real-world scenarios. Candidates are required to demonstrate their expertise in areas such as data processing, data analysis, data integration, and data visualization.

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To pass the Google Professional-Data-Engineer exam, candidates must have a solid understanding of data engineering concepts and techniques, as well as practical experience working with the Google Cloud Platform. They must be able to design and implement data processing systems that are secure, scalable, and efficient, and have the ability to troubleshoot and optimize these systems as needed. Professional-Data-Engineer-JPN exam is challenging and comprehensive, but passing it can open up many career opportunities in data engineering, especially for those interested in working with Google Cloud Platform.

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

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

SectionWeightObjectives
Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Cluster sizing and autoscaling
  • 2. Cost optimization
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- Designing data pipelines
  • 1. AI data enrichment
  • 2. Data acquisition and import
  • 3. Batch processing
  • 4. Integrating with new data sources
  • 5. Streaming (e.g., windowing, late arriving data)
  • 6. Processing logic
- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
Ingesting and processing the data (~20% of the exam)20%- Performing security considerations
  • 1. Auditing, privacy, and compliance
  • 2. Identity and Access Management (IAM)
  • 3. Data encryption
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Building and maintaining data structures and databases
  • 1. Defining data lifecycle
  • 2. Planning for analytical and operational use cases
Storing the data (~20% of the exam)20%- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- 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
- 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
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
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. Workflow orchestration
  • 3. Scheduling jobs

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