Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A developer submits a Spark application to a Databricks cluster. The application creates a SparkSession, reads a CSV file, and calls count() on the resulting DataFrame. Which component is responsible for translating this logical operation into a physical execution plan and coordinating its execution across the cluster?
⚠ Common exam trap
Test-takers frequently confuse the Cluster Manager's resource allocation role with the Driver's planning and coordination responsibilities.
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 Driver, which hosts the SparkSession and the DAG Scheduler.
The Driver is the control plane of a Spark application. It hosts the SparkSession, builds the logical and physical plans through the Catalyst optimizer, and uses the DAG Scheduler to break the plan into stages and tasks that executors run, then aggregates their results for the count().
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The Driver, which hosts the SparkSession and the DAG Scheduler.
Why this is correct
The Driver hosts the SparkSession and runs the DAG Scheduler, which converts the logical plan into stages and tasks, then coordinates their execution. For the count() call, the Driver plans the read and aggregation, schedules tasks on executors, and gathers the final result.
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The Cluster Manager, which allocates containers for the application.
Why it's wrong here
The Cluster Manager provisions resources such as executors on worker nodes, but it does not parse queries or build execution plans. In this scenario, the Cluster Manager only ensures the application has resources; the transformation of count() into tasks happens on the Driver, not in the resource allocator.
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The Executor processes running on worker nodes.
Why it's wrong here
Executors run the individual tasks and store cached data, but they do not build the physical plan or coordinate stages. In this scenario, the driver-side components decide how the count() is executed, and executors simply receive and run the assigned tasks, returning partial results to the driver.
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The Catalog, which stores table and column metadata.
Why it's wrong here
The Catalog maintains metadata about databases, tables, and columns used for name resolution during analysis. It does not generate physical plans or schedule tasks. For the CSV count() operation, the Catalog plays no role in execution planning or cluster 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.