Exam Code: Professional-Data-Engineer
Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

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Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

  • Data publishing and visualization (e.g., BigQuery)
  • Schema design
  • Choice of infrastructure
  • Online (interactive) vs. batch predictions
  • Data modeling
  • Distributed systems
  • Hybrid cloud and edge computing
  • Mapping storage systems to business requirements
  • Designing data pipelines
  • Tradeoffs involving latency, throughput, transactions
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • At least once, in-order, and exactly once, etc., event processing
  • Capacity planning
  • Job automation and orchestration (e.g., Cloud Composer)
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • System availability and fault tolerance
  • Designing data processing systems
  • Selecting the appropriate storage technologies
  • Use of distributed systems

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

SectionWeightObjectives
Designing data processing systems20%- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Designing for scalability and elasticity
  • 3. Selecting appropriate storage solutions
- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Operationalizing machine learning models20%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Handling structured and unstructured data
- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Orchestrating data workflows
  • 3. Transforming and cleaning data
- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources

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