Crucial Exams

GCP Professional Data Engineer Practice Question

A retail analytics team stores 12 TB of daily sales data in a BigQuery table partitioned on transaction_date. Looker Studio dashboards query the table every few minutes, usually calculating SUM(sales) grouped by store_id and product_category while filtering on those same columns. Even with BI Engine enabled, queries now spill to on-demand slots and miss the performance SLO. The team needs the lowest-cost change that will reliably speed up the dashboard while preserving near-real-time freshness. What should they do?

  • Create a BigQuery materialized view that pre-aggregates sales by store_id and product_category and point the dashboards to the materialized view.

  • Add clustering on store_id and product_category to the existing partitioned table and disable BI Engine so queries rely on partition and cluster pruning.

  • Export each daily partition to Cloud Storage, load it into Cloud Bigtable, and query the data from Looker Studio through the Bigtable connector.

  • Increase the BI Engine reservation to twice its current size and leave the dashboards querying the base table.

GCP Professional Data Engineer
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