Professional-Data-Engineer
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Professional-Data-Engineer Exam
Google Certified Professional Data Engineer Exam
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| Topic 1 | - Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
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| Topic 2 | - Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
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| Topic 3 | - Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
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| Topic 4 | - Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
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| Topic 5 | - Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is a highly-revered certification exam that is designed to test individuals' ability to design, build, and manage data processing systems. Professionals who pass Professional-Data-Engineer exam are recognized as experts in the field of data engineering and are highly sought after by leading tech companies worldwide. Professional-Data-Engineer exam is intended for individuals who have a deep understanding of data processing systems and possess the skills to design and manage them.
Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.
