Crucial Exams

GCP Professional Data Engineer Practice Question

A Looker Studio dashboard relies on a BigQuery view that queries a 50-TB date-partitioned table named sales. The table is partitioned on the date column (DATE type). The view applies this filter:

WHERE DATE(order_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)

Users report that the dashboard loads slowly. The BigQuery Query Plan shows that the scan stage reads almost the entire 50-TB table. Without purchasing additional capacity or adding new Google Cloud products, what is the most effective change to remove the scan bottleneck?

  • Enable BigQuery BI Engine with enough in-memory capacity to cache the 50-TB table.

  • Rewrite the predicate to filter on the partition column directly, for example WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY).

  • Purchase additional dedicated slots so the full-table scan finishes faster.

  • Create and query a daily-refreshed materialized view that aggregates the view's results.

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