Here you have the best Databricks Certified Associate Developer for Apache Spark practice exam questions
Which of the following describes the Spark driver?
The Spark driver is the program space where the Spark application's main method runs, coordinating the entire Spark application. It is responsible for managing the application's execution plan, resource allocation, task distribution, and monitoring the application's state throughout its execution. The driver orchestrates the execution process by communicating with the cluster manager to acquire necessary resources and distributing tasks to worker nodes appropriately.
Which of the following describes the relationship between nodes and executors?
An executor is a processing engine running on a node. Executors are worker processes that run on the nodes of a cluster and are responsible for executing tasks assigned by the driver program. They handle data processing and execute the operations required for a Spark application. Hence, the correct relationship is that executors run on nodes.
Which of the following will occur if there are more slots than there are tasks?
If there are more slots than tasks in a Spark job, some slots will remain idle. This will result in inefficient utilization of resources as these idle slots represent unused processing capacity. Thus, the Spark job will not run as efficiently as possible but will still complete successfully.
Which of the following is the most granular level of the Spark execution hierarchy?
In the Spark execution hierarchy, the most granular level is the task. A task represents a unit of work that is executed on a partitioned portion of the data in parallel. Tasks are created by the Spark driver program and assigned to individual executors for execution. Each task operates on a subset of the data and performs a specific operation defined by the Spark application, such as a transformation or an action.
Which of the following statements about Spark jobs is incorrect?
In Spark, jobs are collections of tasks that are divided based on when an action is called, not based on when language variables are defined. This statement is incorrect because it misrepresents how tasks are grouped within a job. Spark creates a job whenever an action (like count() or collect()) is called on a DataFrame or RDD. These jobs are then broken down into stages and tasks, orchestrated based on the logical execution plan derived from the actions, not from the definition of language variables.