Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question
A data engineer is configuring a Lakeflow Job that processes sensitive customer data. The job must notify the on-call team when a run fails and must also capture the run's output for auditing. Which TWO actions should the engineer take in the Lakeflow Jobs configuration? (Choose two.)
⚠ Common exam trap
The trap here is conflating reliability settings such as retries and concurrency limits with notification and audit capabilities, which are separate configuration areas.
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 an email notification for job failure that targets the on-call distribution list.
Failure notifications deliver timely alerts to the on-call team, and preserving run history with exported logs provides the audit trail. Together these native Lakeflow Jobs capabilities meet both the alerting and auditing requirements without adding unnecessary concurrency or retry complexity that would not notify or record anything on its own.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add an email notification for job failure that targets the on-call distribution list.
Why this is correct
Email notifications configured for the failure event send an alert to specified recipients when the job run fails. Targeting the on-call distribution list ensures the team is paged without manual monitoring. This directly satisfies the requirement to notify the team on failure and is a native Lakeflow Jobs notification option.
- ✗
Configure a retry policy with three retries to ensure the on-call team is notified.
Why it's wrong here
Retries attempt to rerun a failed task or job automatically. Retries can mask transient failures and delay notification, and they do not send alerts by themselves. While retries can improve reliability, they do not fulfill the requirement to notify the on-call team on failure or to capture output for auditing.
- ✗
Add a task-level timeout so the job fails faster and generates an alert.
Why it's wrong here
A task timeout bounds how long a task may run before it is marked as failed. It can cause faster failure detection but does not send notifications or store output. Relying on a timeout to generate an alert is indirect and does not address the auditing requirement, so it is not a correct action here.
- ✓
Enable the job's run history and export logs to a durable storage location for auditing.
Why this is correct
Lakeflow Jobs retains run history and provides logs and output for each run. Exporting or retaining these artifacts in durable storage supports auditing and post-incident review. This addresses the requirement to capture the run's output for compliance and troubleshooting purposes without altering the job logic.
- ✗
Set the job's maximum concurrent runs to 1 to prevent overlapping audit records.
Why it's wrong here
Maximum concurrent runs controls how many runs can execute simultaneously. Setting it to 1 prevents overlap but does not create notifications or preserve output for auditing. This setting is about concurrency control, not about alerting or log retention, so it does not satisfy either stated requirement.
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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.