Google Cloud Professional Data Engineer Exam

Using ephemeral clusters is a recommended approach for running multiple small jobs of varying priority on Dataproc because it allows for greater flexibility and cost efficiency. Each ephemeral cluster can be created specifically for a job's requirements and terminated immediately after the job completes. This means you pay only for the resources used during that job, effectively managing costs while adapting to varying workload demands.

Furthermore, since these clusters are spun up specifically for each job, they can be configured with only the necessary resources and dependencies, optimizing performance for each specific task. This model also mitigates resource contention issues that might arise when multiple jobs share the same cluster, allowing higher priority jobs to run without delay.

In contrast, while reusing the same cluster to run jobs in sequence or in parallel might seem efficient, it can lead to resource conflicts or longer wait times for some jobs if they share the same resources. Additionally, using cluster autoscaling could provide some benefits, but it may not fully address issues of resource prioritization and job-specific configurations as effectively as using ephemeral clusters. Hence, the best approach for the scenario described is to utilize ephemeral clusters to maintain operational agility and cost control while managing jobs with varying priorities.