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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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%- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
- Sharing data securely
  • 1. Publishing datasets
  • 2. Data sharing and collaboration
Storing the data (~20% of the exam)20%- Planning for using a data warehouse
  • 1. Defining architecture to support data access patterns
  • 2. Deciding the degree of data normalization
  • 3. Designing the data model
  • 4. Mapping business requirements
- Using a data lake
  • 1. Monitoring the data lake
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Processing data
- Selecting storage systems
  • 1. Lifecycle management of data
  • 2. Analyzing data access patterns
  • 3. Planning for storage costs and performance
- 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
Maintaining and automating data workloads (~15% of the exam)15%- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
- Designing for reliability and fidelity
  • 1. Planning for monitoring and alerting
  • 2. Performing data quality and validation checks
  • 3. Recovering from failures
- Automating data processes
  • 1. Workflow orchestration
  • 2. Scheduling jobs
  • 3. Continuous integration and continuous deployment (CI/CD)
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
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
Designing data processing systems (~30% of the exam)30%- 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 processing resources
  • 1. Cost optimization
  • 2. Cluster sizing and autoscaling
  • 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
- Designing data pipelines
  • 1. Processing logic
  • 2. AI data enrichment
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Batch processing
  • 5. Data acquisition and import
  • 6. Integrating with new data sources

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