In Snowflake, in which scenario is the maximized mode of a multi-cluster warehouse appropriate?

• A. When you have to control the available compute resources, particularly if you have large numbers of concurrent user sessions and/or queries and the numbers do not fluctuate significantly.
• B. When Snowflake should start and stop clusters automatically based on load.
• C. When you want to minimize compute costs by shutting down idle clusters quickly.
• D. When you need to enable automatic data clustering.

Answer: (A)

Maximized mode is about having ready-to-go capacity to handle heavy, steady demand. When you expect a large number of concurrent user sessions and queries and the level of activity isn’t expected to swing wildly, keeping as many clusters online as possible gives you immediate compute resources and consistent performance without the latency of spinning clusters up or down. It gives you tighter control over resource availability, even though it may cost more since more clusters stay active.

If you wanted Snowflake to dynamically adjust clusters based on changing load, you’d use auto-scaling rather than maximizing. If the goal is to reduce costs by shutting idle clusters quickly, you’d favor a more economical approach or aggressive auto-suspend. Automatic data clustering is unrelated to how multi-cluster warehouses scale.