GCP Professional Data Engineer Practice Question

An analytics team maintains a BigQuery dataset. Executives refresh Looker dashboards frequently during business hours. A nightly SQL script transforms 15 TB of raw events into aggregate tables; it only needs to finish before 06:00 the next day. Both workloads currently use the default interactive priority under on-demand pricing. When the ETL overlaps with peak dashboard usage, dashboard queries slow or exceed the interactive-query concurrency limit. Without increasing spend, which change most effectively resolves the contention?

Change the dashboard queries to batch priority so they execute after other workloads complete.
Break the transformation script into smaller interactive queries scheduled hourly and keep all jobs at interactive priority.
Buy 500 dedicated slots through BigQuery Enterprise Edition and reserve them for dashboard workloads.
Run the nightly transformation as a batch-priority job so it is queued at lower priority and does not consume interactive concurrency.

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Answer Description

Submitting the nightly transformation as a batch-priority job is the most effective choice. Batch jobs are queued with lower priority and use the separate batch-query concurrency quota, so they do not count against the project's interactive-query limit. This prevents the ETL from competing with executives' real-time dashboard queries while keeping costs the same under on-demand pricing. Converting the dashboards to batch priority would delay results, purchasing dedicated slots would increase spend, and splitting the ETL into smaller interactive queries would still consume interactive concurrency during peak hours.

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