What feature does Databricks provide to automatically manage the compute resources for scheduled jobs?

Dynamic Cluster Scaling is the feature in Databricks that allows for the automatic management of compute resources for scheduled jobs. This capability enables clusters to automatically scale up or down based on the workload requirements. When the demand increases, Databricks can allocate additional resources to meet the performance needs of running jobs, while it can also reduce resources when the demand decreases, which helps optimize cost and resource utilization.

This feature is particularly beneficial in environments where workloads are variable, allowing organizations to efficiently manage operating expenses without sacrificing performance. It streamlines the process of handling job execution by minimizing the need for manual adjustments to cluster size, ultimately making job management much more efficient and responsive to changing conditions.

While other options like Automatic Scaling of Spark SQL, Serverless Workspaces, and On-Demand Cluster Allocation relate to managing resources or enhancing performance in some ways, they do not specifically encapsulate the dynamic response to workload fluctuations that is characteristic of Dynamic Cluster Scaling.