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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

  • India - 25,42,327 INR
  • United State - 151,247 USD
  • Europe - 135,347 EURO
  • England - 115,632 POUND

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

Build & Operationalize Data Processing Systems

  • Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
  • Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
  • Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.

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

SectionWeightObjectives
Storing the data (~20% of the exam)20%- 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
- 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. Planning for storage costs and performance
  • 2. Analyzing data access patterns
  • 3. Lifecycle management of data
- Using a data lake
  • 1. Managing the lake (data discovery, access, cost controls)
  • 2. Processing data
  • 3. Monitoring the data lake
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
Designing data processing systems (~30% of the exam)30%- 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. Batch processing
  • 2. Integrating with new data sources
  • 3. Data acquisition and import
  • 4. Streaming (e.g., windowing, late arriving data)
  • 5. AI data enrichment
  • 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
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
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
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
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