Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A developer is writing a Spark application that will run on a Databricks cluster. They need to ensure that the driver program can communicate with the executors and that tasks are distributed correctly. Which component is responsible for coordinating the execution of tasks across the executors?
⚠ Common exam trap
Candidates often confuse the cluster manager's resource allocation role with task coordination, which is actually performed by the SparkContext.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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SparkContext
The SparkContext, located in the driver, is the central coordinator for a Spark application. It connects to the cluster manager to request executors, then schedules tasks on those executors via the Task Scheduler. It also manages shared variables and the overall job execution. Without the SparkContext, the application cannot run or distribute tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SparkContext
Why this is correct
The SparkContext is the entry point for Spark functionality and resides in the driver program. It coordinates the execution of tasks by communicating with the cluster manager to acquire executors, and then sends tasks to those executors. It also manages broadcast variables and accumulators. In this scenario, the SparkContext is responsible for the overall coordination.
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Worker Node
Why it's wrong here
Worker nodes are machines that host executors. They do not coordinate task execution; they provide the physical resources for executors to run. The coordination of tasks is handled by the SparkContext in the driver. Worker nodes are part of the cluster infrastructure but not the coordination logic.
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Cluster Manager
Why it's wrong here
The cluster manager (e.g., YARN, Kubernetes) allocates resources to the Spark application, but it does not coordinate task execution. It provides executors to the driver upon request, but once allocated, the SparkContext manages task scheduling and execution. The cluster manager's role is resource allocation, not task coordination.
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Executor
Why it's wrong here
Executors are worker processes that run tasks and store data. They do not coordinate task execution across the cluster; they receive tasks from the driver and execute them. While they report task status back to the driver, they are not responsible for the overall coordination of tasks. The SparkContext in the driver handles coordination.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.