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GCP Professional Data Engineer Practice Question

Your company is migrating a 10-year-old snowflake-style data warehouse to BigQuery. Analysts run ad-hoc dashboards that typically aggregate daily sales by customer attributes and product hierarchy. Query latency must be under two seconds without maintaining complex BI caches, and data is loaded hourly from Cloud Storage. Which data modeling approach in BigQuery best satisfies the latency objective while keeping ETL logic and operating costs low?

  • Retain the star schema unchanged and enable BigQuery BI Engine to cache dashboard queries, avoiding any changes to the underlying tables.

  • Maintain a star schema but create materialized views that pre-join the fact and dimension tables for every common aggregation used in dashboards.

  • Denormalize the warehouse by embedding dimension attributes as nested and repeated fields inside the fact table, producing one wide, partitioned table that analysts query directly.

  • Keep the existing snowflake schema and simply partition the fact table on the load-date column, relying on BigQuery's distributed joins for interactive performance.

GCP Professional Data Engineer
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