Google Professional Data Engineer
Practice questions and flashcards for the Google Professional Data Engineer certification. Covers designing data processing systems, ingesting and processing data (Dataflow, Dataproc, BigQuery, Pub/Sub), storing data (BigQuery, BigLake, AlloyDB, Bigtable, Spanner), preparing data for analysis and ML, and maintaining and automating data workloads.
A data engineering team needs to add automated data quality checks to their Dataform pipelines that prevent deployment of new tables if the data fails validation rules such as uniqueness constraints or referential integrity.
| Domain | Weight | Items | Coverage |
|---|---|---|---|
Designing data processing systems | 22% | 100 items | |
Ingesting and processing the data | 25% | 100 items | |
Storing the data | 20% | 85 items | |
Preparing and using data for analysis | 15% | 66 items | |
Maintaining and automating data workloads | 18% | 100 items |
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