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GCP Professional Data Engineer Practice Question

Your team is building a BigQuery data warehouse for an e-commerce site that records 80 million clickstream events every day. Analysts frequently join these events with the product catalog to calculate conversion rates by brand and style. The product catalog contains about 50 000 rows and its descriptive attributes (price, color, size availability) are updated several times a week. The business wants:

  • Stable query performance for exploratory SQL
  • Minimal operational work when catalog attributes change Which table design best meets these requirements?

  • Fully flatten the product attributes into the clickstream fact table on load; reload the entire fact table each time the catalog is updated to keep data in sync.

  • Embed the full set of product attributes as nested, repeated fields inside every clickstream event and update affected event rows whenever the catalog changes.

  • Store clickstream events in a partitioned, denormalized fact table and maintain the product catalog as a separate dimension table that analysts join at query time.

  • Normalize the schema into multiple dimension tables (product, brand, color) and create a materialized view that joins them to the clickstream fact table on a schedule.

GCP Professional Data Engineer
Storing the data
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