Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版): Professional-Data-Engineer Exam


"Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)", also known as Professional-Data-Engineer exam, is a Google Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 433 Q&As to your Professional-Data-Engineer Exam preparation. In the Professional-Data-Engineer exam resources, you will cover every field and category in Google Cloud Certified Certification helping to ready you for your successful Google Certification.

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Total Questions: 433

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

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Google Professional-Data-Engineer certification exam consists of multiple-choice questions and performance-based tasks that simulate real-world scenarios. Professional-Data-Engineer-JPN exam is divided into four main domains: Designing data processing systems, building and operationalizing data processing systems, integrating data sources, and managing data processing infrastructure.

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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. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Connecting to Looker and other BI tools
  • 2. Preparing data for reporting and dashboards
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. Planning for monitoring and alerting
  • 2. Performing data quality and validation checks
  • 3. Recovering from failures
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Designing the data model
  • 3. Deciding the degree of data normalization
  • 4. Defining architecture to support data access patterns
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Planning for storage costs and performance
  • 3. Analyzing data access patterns
- 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
Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Integrating with new data sources
  • 2. Processing logic
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Data acquisition and import
  • 5. Batch processing
  • 6. AI data enrichment
- Designing data processing resources
  • 1. Cluster sizing and autoscaling
  • 2. Cost optimization
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- 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. Identity and Access Management (IAM)
  • 2. Auditing, privacy, and compliance
  • 3. Data encryption
- 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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