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Google Professional-Data-Engineer certification exam is a comprehensive test that covers a wide range of topics related to data engineering. Professional-Data-Engineer-JPN exam consists of multiple-choice questions that assess the candidates' knowledge of various Google Cloud Platform services, including BigQuery, Cloud Spanner, Cloud Dataflow, and Cloud Dataproc. Additionally, the exam evaluates the candidates' ability to design data processing systems that are scalable, reliable, and secure.

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The Google Professional-Data-Engineer-JPN exam comprises multiple-choice and scenario-based questions that test the candidate's knowledge and skills in different areas of data engineering. The test is designed to assess the candidate's ability to design and build data processing systems that meet specific business requirements. Furthermore, the exam evaluates the candidate's proficiency in data analysis, data visualization, and machine learning.

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

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Google Professional-Data-Engineer exam is an essential certification for professionals in the data engineering field. It demonstrates the candidate's knowledge and expertise in designing, developing, and implementing data solutions using Google Cloud Platform's technologies. Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版) certification is recognized globally and is an excellent way to advance your career in data engineering.

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

SectionWeightObjectives
Maintaining and automating data workloads (~15% of the exam)15%- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Workflow orchestration
  • 3. Scheduling jobs
- Designing for reliability and fidelity
  • 1. Planning for monitoring and alerting
  • 2. Recovering from failures
  • 3. Performing data quality and validation checks
- Monitoring data pipelines and data processes
  • 1. Logging, monitoring, and troubleshooting
  • 2. Managing quotas and resource usage
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
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. Data encryption
  • 2. Auditing, privacy, and compliance
  • 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
Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Lifecycle management of data
  • 3. Planning for storage costs and performance
- Using a data lake
  • 1. Processing data
  • 2. Monitoring the data lake
  • 3. Managing the lake (data discovery, access, cost controls)
- Planning for using a data warehouse
  • 1. Deciding the degree of data normalization
  • 2. Designing the data model
  • 3. Mapping business requirements
  • 4. Defining architecture to support data access patterns
- 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
Designing data processing systems (~30% of the exam)30%- Designing data processing resources
  • 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cluster sizing and autoscaling
  • 3. Cost optimization
- 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 pipelines
  • 1. Batch processing
  • 2. Streaming (e.g., windowing, late arriving data)
  • 3. AI data enrichment
  • 4. Processing logic
  • 5. Data acquisition and import
  • 6. Integrating with new data sources

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