Databricks-Spark-Assoc Spark Architecture and Components Practice Question
In Spark's cluster architecture, what happens to the tasks if the Driver node crashes during the execution of a job?
⚠ Common exam trap
Candidates often assume that if the Driver crashes, executors can continue running independent tasks, misunderstanding the critical supervisory role of the SparkContext.
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 entire application fails and tasks are terminated.
When the Spark Driver fails, the entire SparkContext is terminated. Since the driver is responsible for scheduling tasks and maintaining the application state, all executors lose their connection to the driver, and any pending or running tasks are aborted. The application essentially crashes, and the resources held by the executors will eventually be reclaimed by the cluster manager based on the application's exit status.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Tasks continue to run until the current stage is completed.
Why it's wrong here
Tasks require a constant heartbeat connection to the driver to report progress and receive instructions. Without the driver, the executors have no control plane to coordinate with, so tasks cannot progress, and they will fail or terminate immediately once the connection is lost.
- ✗
The cluster manager automatically restarts the driver and resumes tasks.
Why it's wrong here
The cluster manager manages resources, not the application logic within the driver. While some systems like Kubernetes might restart the driver pod, the SparkContext cannot be recovered automatically, and the application state is lost, requiring a full restart of the entire job from scratch.
- ✓
The entire application fails and tasks are terminated.
Why this is correct
The driver acts as the master process. Its failure results in the loss of the SparkContext, which leads to the immediate termination of the application and all associated tasks. There is no mechanism for executors to continue or recover the work without the driver.
- ✗
Executors take over the driver's role to finish remaining tasks.
Why it's wrong here
Executors are designed only for task processing and do not have the capability to perform scheduling or cluster management duties. They lack the logic required to coordinate the job execution, making it impossible for them to recover the state and continue the work.
About these practice questions
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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.