Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer manages a Lakeflow Job that runs a long-running notebook task on a job cluster. The task occasionally fails due to transient cloud storage errors, and the engineer wants the task to retry automatically without failing the entire job on the first attempt. The engineer also wants to be alerted only if all retries are exhausted. Which configuration should the engineer apply?
⚠ Common exam trap
The trap here is assuming that a job-level timeout or continuous mode provides retries; only task-level retry settings re-execute a failed task within the same run.
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
✓
Set the task's retry count to a value greater than zero and configure a notification for job failure.
Task-level retries re-execute a failed task automatically, so transient storage errors can be absorbed without failing the job immediately. The job is only marked failed after retries are exhausted, at which point a job failure notification alerts the team. This matches the requirement to retry silently and alert only on final failure, unlike cluster or continuous-mode settings that do not target task retries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set the task's retry count to a value greater than zero and configure a notification for job failure.
Why this is correct
Task-level retries allow the task to re-execute automatically when it fails, and the job only reports failure after retries are exhausted. A job failure notification then fires only when the task ultimately fails, matching the alerting requirement. This combination handles transient errors gracefully while avoiding noisy alerts on each retry attempt.
- ✗
Enable continuous mode on the job and configure a notification for run start.
Why it's wrong here
Continuous mode restarts the job after each run, which would rerun the entire pipeline rather than retry the failed task within the same run. It also does not provide the desired alert only after retries are exhausted. Notifying on run start is unrelated to failure conditions and would not satisfy the requirement.
- ✗
Set the cluster's autoscaling to a higher maximum and add a notification for cluster termination.
Why it's wrong here
Autoscaling adjusts compute capacity based on load and has no effect on retrying failed tasks caused by storage errors. Notifying on cluster termination is unrelated to task failure and would not indicate that retries were exhausted. This option misapplies cluster settings to a task reliability problem and does not meet the alerting requirement.
- ✗
Configure a job-level timeout and enable email notifications on every task start.
Why it's wrong here
A job-level timeout cancels the run after a duration but does not retry failed tasks, and notifying on every task start produces excessive alerts unrelated to failures. This does not address transient storage errors or the requirement to alert only after retries are exhausted. The configuration would generate noise and still fail the job without retrying.
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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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