Databricks Data Engineering Professional Exam Practice 2025 – Full Prep Guide

The metric that indicates proper utilization of the VM's resources in a Databricks cluster is CPU Utilization being around 75%. This level of CPU utilization is considered optimal in many computing environments because it signifies a good balance between resource usage and availability. A CPU utilization rate around this percentage implies that the cluster is effectively processing data, fully utilizing the available CPU resources without being overburdened, which could lead to performance throttling.

Maintaining CPU utilization at approximately 75% suggests that there is enough available headroom to accommodate sudden spikes in workload or increased demand without causing delays or resource contention. This is significant in data processing contexts, where workloads can be variable and unpredictable.

In contrast, metrics related to load averages, bytes received, or network I/O may provide insights into the performance of specific components but do not directly signify overall utilization in the same manner as CPU utilization does. For example, a consistent five-minute load average does not inherently indicate optimal resource utilization, as it could reflect low or excessive workloads depending on the context. Similarly, network I/O limits and spikes can be influenced by various factors outside CPU performance, making them less direct indicators of VM resource utilization.