Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer maintains a Lakeflow Job with a scheduled trigger set to run every day at 08:00. The job's source table is refreshed by an upstream process that sometimes finishes later than expected, causing the job to process stale data. The engineer wants the job to start only after the upstream refresh completes, regardless of the clock time, while still preserving the existing 08:00 schedule as a fallback. Which trigger configuration should the engineer implement?
⚠ Common exam trap
The trap here is assuming that a time-based schedule can be replaced by a file arrival trigger, when the requirement actually calls for a table update trigger combined with the existing schedule.
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
✓
Add a table update trigger on the upstream table while keeping the scheduled trigger, so the job runs when the table refreshes or at 08:00.
The requirement is a job that starts when upstream data is ready, yet still runs at 08:00 if that event has not occurred. A table update trigger detects upstream commits and starts the job promptly, while the retained scheduled trigger supplies the fallback. Together they deliver event-driven orchestration with a time-based safety net, which neither a pure schedule nor a polling file trigger provides.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the scheduled trigger to a continuous trigger so the job restarts immediately after each run.
Why it's wrong here
A continuous trigger keeps the job running and restarts it after each completion, which would cause constant reprocessing rather than waiting for upstream data. It ignores the upstream refresh entirely and would consume cluster resources around the clock. It also removes the 08:00 schedule fallback the engineer wants to preserve, so it does not meet the stated goal.
- ✓
Add a table update trigger on the upstream table while keeping the scheduled trigger, so the job runs when the table refreshes or at 08:00.
Why this is correct
Lakeflow Jobs support table update triggers that start a run when a specified table receives a commit, which directly signals upstream completion. Keeping the scheduled trigger provides the fallback at 08:00 in case no update is detected. This combination satisfies both the event-driven and time-based requirements precisely without extra orchestration code.
- ✗
Add a file arrival trigger that monitors the upstream table's storage location and remove the scheduled trigger.
Why it's wrong here
A file arrival trigger fires when new files land at a monitored path, which could approximate upstream completion, but it is not the same as waiting for a table refresh event and it cannot coexist cleanly with the schedule as a fallback. It also introduces polling latency and may fire on partial writes. This does not satisfy the requirement to preserve the 08:00 schedule.
- ✗
Add a run-duration timeout to the scheduled trigger so the job fails if the upstream has not finished by 08:00.
Why it's wrong here
A run-duration timeout limits how long a run may execute before being cancelled; it does not delay the start of a run until upstream data is ready. Configuring a timeout would cause failures instead of coordination, and it provides no mechanism to detect the upstream refresh. This option misapplies a runtime guard as a scheduling dependency.
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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
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Assoc exam.