Databricks-Spark-Assoc Spark Architecture and Components Practice Question
In a Spark application running on a Databricks cluster, the driver program creates a SparkSession and defines a series of transformations. When an action is triggered, the driver requests resources from the cluster manager. Which component is responsible for negotiating and acquiring these resources on behalf of the Spark application?
⚠ Common exam trap
A common mix-up: candidates confuse the role of the cluster manager with that of the driver's internal schedulers, which plan tasks but do not acquire cluster resources.
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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The cluster manager
The cluster manager is the component that allocates resources to the Spark application. The driver requests resources, and the cluster manager launches executors accordingly. The DAG Scheduler and Task Scheduler operate within the driver to plan and schedule tasks, while executors run the tasks but do not negotiate resources.
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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The Task Scheduler
Why it's wrong here
The Task Scheduler is responsible for scheduling individual tasks onto executors, not for acquiring resources from the cluster manager. It works within the driver, using the resources already provided. It does not negotiate with the cluster manager for additional executors or memory.
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The DAG Scheduler
Why it's wrong here
The DAG Scheduler transforms the logical execution plan into stages of tasks but does not handle resource negotiation. It operates within the driver after resources are already allocated. It schedules tasks onto existing executors but does not communicate with the cluster manager to acquire new resources.
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The cluster manager
Why this is correct
The cluster manager (e.g., YARN, Kubernetes, or Databricks' internal manager) is responsible for allocating resources such as executors to the Spark application. The driver communicates with the cluster manager to request containers or pods, which then launch executors. This is a core part of Spark's architecture.
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The Executor
Why it's wrong here
Executors are worker processes that run tasks and store data. They do not negotiate resources; they are launched by the cluster manager upon request from the driver. Executors report their status to the driver but do not participate in resource acquisition from the cluster manager.
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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.