Which cluster mode involves running Spark locally without worker nodes?

The choice of the single node cluster is correct because it refers to a configuration where Spark runs in a standalone mode on a single node, functioning as both the driver and executor. This mode is useful for development, testing, or scenarios where the resources of a single machine are sufficient to handle the job at hand. In a single-node cluster, there are no additional worker nodes; all processing occurs locally on the machine.

This setup simplifies the architecture since it eliminates the complexity of a distributed environment, allowing developers to run Spark applications without the need for a full cluster setup. It is particularly advantageous for smaller datasets or during the initial stages of application development, where quick iterations are needed.

The other options involve multiple nodes or configurations that include more complexity, which does not apply to the single-node context. Multi-node clusters, standard clusters, and custom clusters typically involve multiple worker nodes and are intended for larger scale processing with distributed computing capabilities.