Databricks Data Engineering Professional Practice Exam 2026 - Free Data Engineering Practice Questions and Study Guide

The ideal job configuration in this scenario is scheduling a job to execute every hour on a new job cluster. This approach aligns perfectly with the need to update dashboards every hour while also being cost-effective. Using a new job cluster ensures that resources are provisioned only when needed, thus optimizing costs compared to keeping an interactive cluster running continuously. This on-demand resource allocation allows processing jobs to run efficiently without incurring unnecessary charges during idle times.

Moreover, executing the job every hour guarantees that the dashboards receive fresh data as stipulated by the service-level agreement, ensuring timely updates without needing constant resource allocation. This balance of operational efficiency and cost management is crucial in data engineering environments, especially when adhering to specific update frequencies.

In contrast, frequent triggers based on data landing or scheduling on a dedicated interactive cluster could lead to increased costs or inefficient resource utilization, especially if those resources are kept running continuously without need.