Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Which component in the Spark cluster architecture is responsible for communicating directly with the Cluster Manager (e.g., YARN, Mesos, K8s) to request and release resources?
⚠ Common exam trap
Candidates incorrectly select the 'Executor' or 'DAG Scheduler' as the component that negotiates resources, failing to realize the Driver acts as the sole intermediary.
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 Spark Driver is responsible for resource negotiation with the underlying Cluster Manager. It maintains the SparkContext and coordinates with the resource manager to acquire executors for the application. Once executors are acquired, the driver schedules tasks on them. This role is fundamental to the driver-executor model, as the driver serves as the central control point for the application's lifecycle and hardware interaction.
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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Executor
Why it's wrong here
Executors are worker processes that execute tasks assigned by the driver. They do not communicate with the cluster manager to request resources; rather, they report their health and status back to the driver through heartbeat signals once they have been initialized by the cluster manager.
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Worker Node
Why it's wrong here
The worker node is a physical or virtual machine in the cluster. While it hosts executors, it does not negotiate resources with the cluster manager. That responsibility lies with the Spark Driver, which acts as the application master to request the necessary compute resources from the manager.
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Spark Driver
Why this is correct
The Spark Driver contains the SparkContext and the scheduler backends. It acts as the application master, negotiating with the cluster manager to acquire executors. Without the driver, the Spark application would have no way to obtain the compute resources required to process data in a distributed manner.
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Task Scheduler
Why it's wrong here
The Task Scheduler is a component internal to the driver. It handles the scheduling of individual tasks within the stages of a job, but it relies on the Scheduler Backend to communicate with the cluster manager for resource allocation. It does not interface directly with the manager.
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JA
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.