Data Engineering Associate with Databricks Practice Exam

How does work get processed across nodes in a Databricks cluster?

1. The coordinator node performs all tasks

2. Workers report data to the driver only

3. The driver node assigns tasks to executors

4. All nodes process the same data simultaneously

The correct answer is: The driver node assigns tasks to executors

Work in a Databricks cluster is processed through a distinct architecture that involves various nodes working collaboratively. The driver node is integral to this process as it is responsible for orchestrating the execution of tasks. It acts as the central control point where the main program runs. The driver computes the overall plan and then breaks it down into smaller tasks that can be distributed across worker nodes, which are also referred to as executors. Once the driver has broken down the tasks, it assigns these smaller tasks to the available executors on the worker nodes. Each executor processes its assigned task independently, using local resources such as memory and CPU, and then reports back the results to the driver. This architecture allows for parallel processing, leading to efficient job execution and utilization of the cluster's resources. In contrast, the other options describe incorrect or incomplete aspects of how tasks are managed within a Databricks cluster. For instance, stating that a coordinator node performs all tasks overlooks that the work is distributed among multiple nodes for efficiency. Similarly, claiming that workers only report data to the driver fails to recognize that they are actively engaged in processing tasks assigned by the driver. Finally, suggesting that all nodes process the same data simultaneously misrepresents the concept of task distribution where different nodes