Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is using Databricks Jobs to run a nightly ETL pipeline. The job occasionally fails due to a transient network error when writing to an external database. The engineer wants to automatically retry the job a few times before marking it as failed. What is the most efficient way to configure this in Databricks?
⚠ Common exam trap
Candidates often confuse concurrent runs with retries; concurrent runs allow parallel executions, while retries automatically re-run a failed job sequentially.
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
✓
Configure the job to retry on failure with a specified number of retries and interval.
Databricks Jobs offer a built-in retry policy that automatically re-runs a failed job a specified number of times with a defined interval. This is ideal for handling transient errors like network issues. Configuring retries at the job level is efficient, requires no code changes, and integrates with job monitoring and alerting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Databricks notebook to catch exceptions and loop until the write succeeds.
Why it's wrong here
Implementing a custom retry loop in a notebook is possible but not the most efficient or maintainable approach. It requires additional code and error handling, and it may not integrate well with job-level monitoring and alerting. Databricks Jobs provide built-in retry policies that are simpler and more reliable for this purpose.
- ✓
Configure the job to retry on failure with a specified number of retries and interval.
Why this is correct
Databricks Jobs support automatic retries on failure. By configuring the retry policy with a maximum number of retries and an interval between attempts, the job will automatically re-run if it fails due to transient errors. This is the most efficient and native way to handle transient failures without manual intervention.
- ✗
Set the job's timeout to a higher value to give the write operation more time to complete.
Why it's wrong here
Increasing the timeout only extends the allowed duration before the job is killed; it does not retry the job on failure. If the network error causes a failure, a longer timeout will not help. The job will still fail after the timeout expires. Retry configuration is the appropriate solution for transient errors.
- ✗
Set the job's maximum concurrent runs to 3 to allow multiple attempts.
Why it's wrong here
Maximum concurrent runs controls how many instances of the job can run simultaneously. It does not trigger retries on failure. Setting it to 3 would allow multiple overlapping runs, which could cause data duplication or conflicts, but it would not automatically retry a failed run. This is not the correct mechanism for retrying on failure.
About these practice questions
One of 276 original Databricks-DE-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
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.