Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineering team runs a nightly Lakeflow Job that ingests files from cloud storage, transforms them with a notebook, and then runs a SQL task. The team wants the SQL task to execute only after the notebook transform succeeds, but they do not want the SQL task to wait for a fixed delay. Which Lakeflow Jobs feature should they configure on the SQL task?
⚠ Common exam trap
The trap here is assuming that scheduling a task later in time or checking for an output file creates a dependency, when Lakeflow Jobs requires an explicit depends_on relationship to order tasks.
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 SQL task's depends_on property to reference the notebook transform task.
The depends_on property is the native Lakeflow Jobs mechanism for expressing task-level dependencies. By setting depends_on on the SQL task to point at the notebook transform task, the SQL task is scheduled only after the transform completes successfully. This avoids fixed delays, avoids polling, and keeps the pipeline deterministic and observable within a single job run.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a time-based trigger on the SQL task using a cron expression that starts two minutes after the job run begins.
Why it's wrong here
Cron schedules apply to jobs, not individual tasks, and they trigger job runs at wall-clock times. A cron expression cannot make a task wait for another task's completion, and a fixed two-minute offset is fragile if the transform takes longer or shorter. This does not create a true dependency.
- ✓
Set the SQL task's depends_on property to reference the notebook transform task.
Why this is correct
The depends_on property on a task creates an explicit dependency on another task in the same job. When the SQL task lists the notebook transform task in depends_on, the SQL task will not start until the transform task completes successfully. This enforces the required ordering without introducing a fixed delay or polling mechanism.
- ✗
Add a run_if condition to the SQL task that checks whether the transform task's output path exists.
Why it's wrong here
The run_if condition controls whether a task runs based on the outcome of another task, but it is not the mechanism that establishes ordering. Without depends_on, the SQL task has no dependency edge and may start in parallel. Checking a path also introduces race conditions and does not guarantee the transform has fully committed.
- ✗
Place the SQL task and the notebook transform task in separate jobs and chain them using a job-level trigger.
Why it's wrong here
Splitting the work into separate jobs and chaining them adds operational overhead and still does not define an intra-job dependency. A job-level trigger may start the second job after the first finishes, but the scenario is about tasks within one Lakeflow Job. This approach also complicates monitoring and retries.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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