BigQuery query costs are high across multiple business units. Which two methods help control costs? (Choose two.)

Enforcing user- or project-level custom query quotas directly limits the amount of data processed or the number of queries run, preventing unexpected cost spikes. Switching from on-demand to flat-rate pricing provides predictable costs, as you pay a fixed amount for dedicated query capacity (slots) rather than per terabyte processed. This allows better budget planning and can be more cost-effective for high usage. Partitioning users into separate projects or maintaining separate physical copies of datasets doesn't inherently control query costs; it primarily aids in access control and data isolation. Splitting the data warehouse into multiple data warehouses could increase management overhead and might not reduce query costs if the same amount of data is still being processed.