Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer has a Lakeflow Job with a notebook task that occasionally fails due to transient network errors when reading from an external REST API. The engineer wants the task to automatically retry up to three times, but only for this specific task, without affecting other tasks in the job. What should the engineer do?
⚠ Common exam trap
Many candidates confuse task-level retries with job-level concurrency or timeout settings, which do not control automatic retries.
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 Retries to 3 in the task configuration.
The correct approach is to set the Retries field on the specific task to 3. This is a built-in feature of Lakeflow Jobs that applies per task, so other tasks remain unaffected. It is the simplest and most direct way to handle transient failures without modifying code or job-level settings.
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 Retries to 3 in the task configuration.
Why this is correct
In Lakeflow Jobs, each task has a Retries field that specifies how many times the task should be retried if it fails. Setting it to 3 on the specific task ensures only that task retries, leaving other tasks unaffected. This is the direct and supported method for per-task retry behavior.
- ✗
Configure the task's Retries setting to 3 and add a retry condition using the task's timeout.
Why it's wrong here
The task timeout only limits how long the task can run before being cancelled; it does not control retry behavior. Setting Retries to 3 is correct, but adding a retry condition via timeout is not a supported configuration. Retries are configured directly on the task, and there is no separate retry condition tied to timeout in Lakeflow Jobs.
- ✗
Use a notebook %run magic command to wrap the API call in a loop that retries three times.
Why it's wrong here
%run is used to execute another notebook and return its results; it is not a retry mechanism. Implementing a loop inside the notebook could handle retries, but it is a manual coding approach and not the built-in task-level retry feature. The scenario asks for the simplest configuration, which is the task's Retries setting.
- ✗
Set the job-level maximum concurrent runs to 3 and enable retries.
Why it's wrong here
Maximum concurrent runs controls how many instances of the job can run simultaneously; it does not govern retries of a failed task. Enabling retries at the job level is not a setting; retries are configured per task. This approach would not achieve the desired automatic retry of the specific task.
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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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