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Exam Topics
The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:
Topic 1. Designing Data Processing Systems
To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems
The following will be discussed here:
- Choice of infrastructure
- Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
- Schema design
- Job automation and orchestration (e.g., Cloud Composer)
- Data modeling
- At least once, in-order, and exactly once, etc., event processing
- Online (interactive) vs. batch predictions
- Data publishing and visualization (e.g., BigQuery)
- Designing data pipelines
- Designing data processing systems
- Selecting the appropriate storage technologies
- Capacity planning
- Hybrid cloud and edge computing
- System availability and fault tolerance
- Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
- Mapping storage systems to business requirements
- Distributed systems
- Tradeoffs involving latency, throughput, transactions
- Use of distributed systems
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Topic 2: Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Topic 3: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Topic 4: Ensuring solution quality | 28% | - Security and governance
|







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