Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer has a Lakeflow Job with a linear dependency chain: Task A, then Task B, then Task C. Task B sometimes fails due to transient errors. The engineer wants Task C to run only if Task B succeeds, but also wants Task B to be retried automatically before considering the job failed. Which configuration should they use?
⚠ Common exam trap
The trap here is choosing a dependency condition that ignores success, such as 'All done', while focusing only on 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 Task B's Retries to 2 and configure Task C to depend on Task B with the default 'All succeeded' condition.
To automatically retry Task B on transient failures, set its Retries to a positive number. To ensure Task C runs only if Task B succeeds, use the default dependency condition 'All succeeded' for Task C. This combination provides retries for Task B and conditional execution for Task C. Other conditions like 'All done' or 'At least one failed' would cause Task C to run in undesired situations.
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 Task B's Retries to 2 and configure Task C to depend on Task B with the 'At least one failed' condition.
Why it's wrong here
The 'At least one failed' condition triggers Task C only if at least one upstream task failed. This is the opposite of the requirement: Task C should run only if Task B succeeds. Using this condition would cause Task C to run when Task B fails, and not run when Task B succeeds. The retries setting is appropriate, but the dependency condition is incorrect for the scenario.
- ✗
Set Task B's Retries to 0 and configure Task C to depend on Task B with the 'At least one succeeded' condition.
Why it's wrong here
With Retries set to 0, Task B will not be retried automatically, so a transient failure would immediately fail the task. The 'At least one succeeded' condition is used for tasks with multiple upstream dependencies; with a single dependency, it behaves similarly to 'All succeeded', but the lack of retries means the requirement for automatic retries is not met. Task C might still run if Task B succeeds, but transient failures would not be handled.
- ✓
Set Task B's Retries to 2 and configure Task C to depend on Task B with the default 'All succeeded' condition.
Why this is correct
Task B's Retries setting will automatically reattempt the task if it fails, up to the specified count. Task C's default dependency condition 'All succeeded' means it runs only if Task B ultimately succeeds after retries. If Task B fails after all retries, Task C will not run, and the job will be marked as failed. This matches the requirement exactly.
- ✗
Set Task B's Retries to 2 and configure Task C to depend on Task B with the 'All done' condition.
Why it's wrong here
The 'All done' condition means Task C runs after Task B completes, regardless of whether Task B succeeded or failed. This violates the requirement that Task C should run only if Task B succeeds. Even with retries, if Task B ultimately fails, Task C would still run under 'All done', which is not desired. The retry setting is correct, but the dependency condition is wrong.
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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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