What is the MOST cost-effective solution to optimize query execution in Amazon Redshift during peak usage hours?

Enabling concurrency scaling in the workload management (WLM) queue is the most cost-effective solution for optimizing query execution in Amazon Redshift during peak usage hours because it directly addresses the need for managing multiple concurrent queries without requiring additional dedicated resources. Concurrency scaling allows Amazon Redshift to automatically add transient capacity to handle sudden spikes in demand. This means that during periods of high workloads, Redshift can temporarily allocate additional compute resources to handle the overflow of queries, ensuring that performance remains optimal without incurring long-term costs associated with adding more nodes.

This option allows users to efficiently manage costs since the additional capacity is only utilized during peak times and automatically scaled back down when demand decreases. Therefore, you are paying specifically for the extra resources only when they are needed and not on a continual basis, making it more economical.

The other approaches might seem viable but involve more significant resource management and costs. For example, adding more nodes during peak hours can lead to increased fixed costs, and using elastic resize involves more complex operations and potential downtime. Utilizing snapshots, restore, and resize also complicates the environment and can lead to delays, as this process is not instantaneous and can lead to a lack of service availability during the operation. Consequently, leveraging concurrency scaling provides a targeted