PDF Version DemoBuilding & Operationalizing Data Processing Systems
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.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality | 28% | - Security and governance
|
| Topic 2: Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Topic 3: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Topic 4: Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
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