PDF Version DemoThe candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:
Design Data Processing Systems
- Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
- Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;
- Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
- Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
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Preparation Process
To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.
Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
| Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
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