Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
When a Data Engineer uses a 'Repair and Rerun' functionality on a failed Databricks Job, what happens?
⚠ Common exam trap
Candidates often assume 'Repair and Rerun' restarts the entire job from the very beginning, failing to realize it is designed specifically to resume only from the point of failure.
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
✓
It restarts from the failed task.
Repair and Rerun is a critical feature for managing complex DAG workflows. It allows engineers to restart a job from the specific failed task, inheriting the successful results of prior tasks. This saves significant time and compute costs by avoiding redundant processing of already completed data, which is essential for maintaining efficient pipelines and preventing unnecessary data re-computation in production environments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It deletes all previous successful task logs.
Why it's wrong here
Repair and Rerun preserves the history and logs of the original run. The failed tasks are essentially retried, but the successful tasks remain in the job history, allowing for proper audit trails and debugging of why the initial run failed versus the final successful outcome.
- ✓
It restarts from the failed task.
Why this is correct
The Repair and Rerun functionality intelligently resumes the workflow starting from the point of failure. It uses the output of previously successful tasks, ensuring that only the failed task and its downstream dependencies are executed, which optimizes compute usage and speeds up the time to recovery for pipelines.
- ✗
It resets all task statuses to 'Pending'.
Why it's wrong here
Resetting all task statuses would imply a full job restart, which is not what Repair and Rerun does. It selectively executes only the failed tasks and downstream dependencies, keeping the status of successfully completed tasks intact to ensure that the pipeline does not re-process data unnecessarily.
- ✗
It requires a new cluster to be provisioned.
Why it's wrong here
Repair and Rerun typically reuses the cluster configuration defined in the job. It does not force the creation of a new cluster unless the original cluster configuration is changed. This ensures consistency in the environment where the tasks are executed, preserving the same libraries and Spark settings.
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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
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