Databricks-DE-Pro Debugging and Deploying Practice Question
A production Databricks workflow involves a task that runs a notebook. The notebook takes 15 minutes to finish, but the workflow is set to timeout after 10 minutes. What happens?
⚠ Common exam trap
Candidates often assume that a workflow timeout will gracefully cancel the notebook and mark it as 'Canceled' or 'Timed Out', overlooking that Databricks specifically categorizes exceeded task timeouts as 'Failed'.
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 task is terminated and marked as 'Failed' by the scheduler.
When a task exceeds its configured 'timeout' value, the Databricks scheduler forcibly terminates the task execution. This is a deliberate safety measure to prevent runaway processes from consuming cluster resources indefinitely. In production, this highlights the necessity of monitoring execution times and setting appropriate timeouts that account for normal data volume fluctuations while catching truly stuck jobs that could impact cost and resource availability.
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 workflow continues to run, but logs a warning about the duration.
Why it's wrong here
Databricks workflows are strict about timeouts; they do not simply log warnings and continue. If a task exceeds its allocated duration, the platform stops the execution process to protect the cluster from resource exhaustion, making this an immediate failure rather than a minor warning.
- ✓
The task is terminated and marked as 'Failed' by the scheduler.
Why this is correct
The scheduler enforces the timeout by killing the task process. Once terminated, the job workflow marks the task as 'Failed' because it did not complete successfully within the defined limits. This ensures that the system does not waste time and money on jobs that are behaving abnormally.
- ✗
The workflow pauses and waits for the engineer to manually approve continuation.
Why it's wrong here
Databricks workflows do not have a built-in 'pause and wait for approval' feature for task timeouts. The system is designed for automated execution, and timeouts are terminal events. Expecting manual intervention would defeat the purpose of an automated pipeline, which is meant to run without constant human monitoring.
- ✗
The task continues to run, but is moved to a background queue.
Why it's wrong here
There is no 'background queue' for tasks that have timed out. Once a task reaches its timeout limit, the scheduler terminates the job context entirely. The task does not 'background' itself; it is treated as a failed job execution, requiring the engineer to adjust the timeout or optimize the code.
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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-DE-Pro 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-DE-Pro exam.