Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A data engineer submits a Spark application using spark-submit in client deploy mode from an edge node. The application reads a large Parquet dataset, performs a groupBy aggregation, and writes the result to a Delta table. The engineer notices that the Driver process runs on the edge node and remains alive throughout the application's lifetime. Which statement best describes the role of the Driver in this scenario?
⚠ Common exam trap
The trap here is assuming that because the Driver runs on the edge node, it also performs data processing or storage, when in fact it only coordinates.
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
✓
The Driver schedules tasks, maintains the DAG, and coordinates with the cluster manager to allocate executors.
The Driver in client deploy mode runs on the submitting host and orchestrates the application: it builds the DAG, schedules stages and tasks, and communicates with the cluster manager to acquire executors. It does not execute data processing tasks or store data. Therefore, the statement that it schedules tasks and coordinates resource allocation is correct.
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 executes the actual data processing tasks and stores intermediate shuffle data on local disk.
Why it's wrong here
Data processing tasks are executed by executors, not the Driver. Executors read data, perform transformations, and write shuffle files to local disk. The Driver only coordinates and does not process the distributed data. In this scenario, the Driver on the edge node would not handle the groupBy aggregation directly.
- ✗
The Driver is responsible for storing the final output data and serving it to downstream consumers.
Why it's wrong here
The Driver does not store final output data; that is written by executors to the specified sink (e.g., Delta table). The Driver coordinates the write but does not hold the data. In this scenario, the output is written by executors, not the Driver on the edge node.
- ✓
The Driver schedules tasks, maintains the DAG, and coordinates with the cluster manager to allocate executors.
Why this is correct
In client deploy mode, the Driver runs on the submitting machine (edge node) and is responsible for converting the user program into a DAG, splitting it into stages, scheduling tasks on executors, and negotiating resources with the cluster manager. It also tracks task status and aggregates results. This matches the scenario where the Driver remains alive on the edge node.
- ✗
The Driver acts as a passive monitor that only collects metrics and logs, while the cluster manager handles all scheduling.
Why it's wrong here
The Driver is not passive; it actively builds the execution plan, schedules tasks, and manages job progress. The cluster manager allocates resources but does not schedule Spark tasks. The Driver's role includes DAG scheduling and task coordination, which are essential for the application to run.
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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.