Which types of clusters can be created in Databricks?

Databricks offers a variety of cluster types, including both interactive and job clusters, which allows users to have flexibility in how they execute their data workflows. Interactive clusters are designed for users who need to run commands and queries in real-time, often for exploration and development. They are typically used within notebooks or for data analysis activities, where the user is actively working with the data.

On the other hand, job clusters are intended for running automated jobs such as ETL processes, batch processing, and other scheduled tasks that don’t require direct user interaction during execution. Each type of cluster serves different use cases, enhancing productivity and resource management.

The option indicating only interactive clusters would limit the capability to only real-time interactive tasks, while the exclusive mention of job clusters would disregard the exploratory and interactive functionalities. Similarly, focusing solely on GPU clusters narrows the variety of tasks that can be performed, as not all analytics or processing requires GPU acceleration. Thus, by recognizing the availability of multiple cluster types within Databricks, users can optimize their analytical processes according to their specific needs.