Exam Certified Associate Developer for Apache Spark topic 1 question 1 discussion

The Spark driver is responsible for orchestrating the execution of a Spark application, managing the SparkContext, and coordinating the execution of tasks across the Spark cluster. It does not perform the execution of tasks itself but rather schedules tasks on the worker nodes. The Spark driver is not fault-tolerant in the sense that if it fails, the entire Spark application usually fails. It also does not scale horizontally; only the executors (worker nodes) do that.

ignore my previous comment. E is the answer as per the sample exam in Databricks site.

B is the answer as per the sample exam in Databricks site.

D is the answer

Answer -D

Please let me know what are these dumps used for? Scala - Spark or Python Spark?

Answer: D Receives the user's code and breaks it into tasks for execution. Orchestrates the execution plan and optimizes the Spark job. Coordinates with cluster managers to allocate resources for tasks. Collects and aggregates results from distributed workers. Maintains the metadata and state of the Spark application during its execution.

The correct answer is D. The Spark driver is the program space in which the Spark application's main method runs, coordinating the entire Spark application. Explanation: The Spark driver is a program that runs the main method of a Spark application and coordinates the execution of the entire application. It is responsible for defining the SparkContext, which is the entry point for any Spark functionality. The driver program is responsible for dividing the Spark application into tasks, scheduling them on the cluster, and managing the overall execution. The driver communicates with the cluster manager to allocate resources and coordinate the distribution of tasks to the worker nodes. It also maintains the overall control and monitoring of the application. Horizontal scaling, fault tolerance, and execution modes are not directly related to the Spark driver.

D. The Spark driver is the program space in which the Spark application’s main method runs coordinating the Spark entire application. The Spark driver is responsible for coordinating the execution of a Spark application, and it runs in the program space where the Spark application's main method runs. It manages the scheduling, distribution, and monitoring of tasks across the cluster, and it communicates with the cluster manager to acquire resources and allocate them to the application. The driver also maintains the state of the application and collects results. It is not responsible for performing all execution in all execution modes, nor is it fault-tolerant or horizontally scalable.