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GCP Professional Data Engineer Practice Question

Your retail analytics team ingests 5 TB of point-of-sale records into a single BigQuery table every day. Analysts typically run interactive SQL that filters on sale_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE() and aggregates by store_id and product_category. Queries are becoming slower and more expensive, but the upstream ingestion pipeline cannot be modified. Which BigQuery design change will most effectively reduce both query latency and bytes scanned costs?

  • Create a materialized view that pre-aggregates sales for the last 90 days and refresh it on demand.

  • Convert the table to a partitioned table on sale_date and add clustering columns for store_id and product_category.

  • Replace the single table with daily sharded tables named sales_YYYYMMDD and query them with table wildcards.

  • Normalize the schema by moving store and product attributes into separate dimension tables and leaving only foreign keys in the fact table.

GCP Professional Data Engineer
Storing the data
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