Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which component in the Spark architecture is responsible for scheduling tasks and managing the execution of jobs on the cluster?
⚠ Common exam trap
Candidates frequently mix up the roles of the Driver and the Executors, incorrectly attributing task scheduling and job coordination responsibilities to worker nodes.
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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Spark Driver
The Driver process is the central coordinator in Spark. It hosts the SparkContext, which converts user code into a Directed Acyclic Graph (DAG) and schedules tasks across the executors. Understanding this role is vital because it explains why the Driver can become a bottleneck if it handles too much data locally or manages excessive partitions, directly impacting the overall job latency and system stability in distributed environments.
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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Cluster Manager
Why it's wrong here
The Cluster Manager, such as YARN, Mesos, or Kubernetes, is responsible for acquiring physical resources like CPU and memory from the underlying infrastructure. It does not handle the internal logic of task scheduling, DAG decomposition, or the coordination of specific Spark job stages within the application context.
- ✓
Spark Driver
Why this is correct
The Driver creates the SparkSession, translates transformations and actions into a DAG, and orchestrates the execution of tasks across the worker nodes. It maintains information about the state of the executors and ensures that data processing occurs efficiently by optimizing the execution plan before dispatching it to executors.
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Spark Executor
Why it's wrong here
The Executor is a JVM process running on worker nodes that performs the actual data processing tasks assigned by the Driver. Executors do not manage scheduling or job orchestration; their primary responsibility is to execute code, store cached data, and report progress back to the central Driver node.
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Storage Manager
Why it's wrong here
The Storage Manager is an internal subsystem within an Executor that handles data persistence, such as caching RDDs or DataFrames in memory or on disk. It does not manage task scheduling or job-level orchestration across the cluster, as those functions are strictly reserved for the Spark Driver process.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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