Which metric would indicate a potential bottleneck on the driver in a Databricks production cluster?

The indication of a potential bottleneck on the driver in a Databricks production cluster is reflected in the overall cluster CPU utilization being around 25%. This relatively low CPU utilization suggests that the driver is not efficiently utilizing its processing capacity and may be underperforming. Ideally, a well-functioning cluster should exhibit higher CPU utilization, especially during peak workloads, indicating that resources are actively engaged in processing tasks.

When CPU utilization is low while there are ongoing job executions, it signals that the driver might be facing limitations elsewhere, such as memory constraints, poor task scheduling, or inefficiencies in the data handling process. This scenario can lead to the driver not keeping up with tasks, thus creating a bottleneck that affects overall job performance and response times.

The other options — the five-minute load average being flat, bytes received being capped at 80 million bytes per second, and total disk space remaining constant — do not directly indicate a bottleneck in the driver. A flat load average, for example, may imply stability rather than an issue. The bytes received metric being within a limit could be indicative of a network capacity, and consistent disk space does not necessarily correlate with driver performance unless it is critically low, which the option does not imply. Therefore, the