Google Cloud Certified Professional Data Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose B1

  The right answer is B becasue the minimum partition time ramnge is 1 hour: https://docs.cloud.google.com/bigquery/docs/partitioned-tables#ingestion_time

• Chose A2

  BigQuery is a powerful data warehouse designed for analyzing large datasets efficiently. Partitioning tables allows you to manage large datasets by splitting them into segments based on a key, such as time. By creating a partitioned table and updating it every 30 minutes, you can load the new price data directly into the correct partitions. BigQuery’s partitioned tables optimize both the storage and querying cost because BigQuery only scans the relevant partitions when querying, minimizing the amount of data read and hence reducing costs. Partitioning by time (e.g., timestamp or date columns) is particularly effective for datasets with periodic updates (like price data) since each batch of data will be loaded into the corresponding partition.

• Chose B2

  Actually, in this question, I think B is the most suitable. C, D are somehow overkill. A due to the minimum partition granularity. However, with B, the data could not be previewd also it is not possible to estimate the cost.