Google Cloud Professional Data Engineer Exam

In Dataproc, under what circumstances should you enable autoscaling?

1. When you want to scale on-cluster Hadoop Distributed File System (HDFS).

2. When you want to scale out single-job clusters.

3. When you want to down-scale idle clusters to minimum size.

4. When there are different size workloads on the cluster.

The correct answer is: When you want to scale out single-job clusters.

Enabling autoscaling in Dataproc is particularly advantageous for managing workloads in dynamic environments, especially when scaling out single-job clusters. When a cluster is dedicated to running a single job, autoscaling allows you to allocate additional resources automatically during the job’s execution phase based on its needs and workload characteristics. This ensures that the job can utilize more computing power when required, leading to faster processing times and efficient resource utilization. The ability to automatically adjust the size of the cluster means that it can flexibly accommodate the changing resource demands of the job, improving performance and potentially reducing costs by avoiding over-provisioning resources. This is particularly useful for large-scale data processing tasks where the computational requirements may vary throughout the job. In contrast, scaling on-cluster Hadoop Distributed File System (HDFS) does not directly apply because HDFS does not require scaling in the same manner that compute resources do. On the other hand, down-scaling idle clusters to minimum size may be a part of cost management but does not specifically leverage the advantages of autoscaling during job execution. Finally, while different size workloads may benefit from autoscaling, the mention of single-job clusters clarifies its application and effectiveness in that scenario. This targeted approach ensures that resources are efficiently managed without unnecessary