Databricks-DE-Assoc Troubleshooting, Monitoring, and Optimization Practice Question
A data engineer is investigating a Databricks job that failed overnight. The job's status in the Jobs UI shows 'Failed', and the engineer needs to view the error message and stack trace to determine the cause. Where should the engineer look to find the detailed error information for the failed run?
⚠ Common exam trap
Test-takers frequently confuse infrastructure logs like cluster event logs with application logs; cluster events won't show the Python or Scala exception that caused the job to fail.
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
✓
In the driver logs, accessible from the run's detail page in the Jobs UI.
Driver logs are the definitive source for application-level errors in Databricks jobs. They capture stdout and stderr from the driver, including full stack traces. The Jobs UI provides direct access to these logs from the run's detail page, making it the first place to check when a job fails due to an exception.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
In the driver logs, accessible from the run's detail page in the Jobs UI.
Why this is correct
The driver logs contain the standard output and error streams from the Spark driver, including exception stack traces and error messages. In the Jobs UI, each run has a detail page with links to logs. This is the primary location to find why the job failed, as it captures the exact error thrown during execution.
- ✗
In the cluster's event log, which records all cluster scaling and termination events.
Why it's wrong here
The cluster event log records infrastructure events like node additions, removals, and termination reasons. It does not contain application-level error messages or stack traces from the job's code. While it might indicate if the cluster failed, it won't explain a code exception. Therefore, it is not the right place to find the detailed error for a job failure.
- ✗
In the Databricks audit logs, which record user actions and API calls.
Why it's wrong here
Audit logs track administrative activities such as job creation, deletion, and permission changes. They do not capture runtime errors or stack traces from job execution. While useful for security and compliance, they are not a source for debugging code failures. The engineer should consult driver logs instead.
- ✗
In the Spark UI's SQL tab, which shows the query plan and execution metrics.
Why it's wrong here
The SQL tab in the Spark UI displays query plans, stages, and metrics for SQL and DataFrame operations. It can help diagnose performance issues but does not show application error messages or stack traces. A job failure due to an exception will not be explained here; you would need to look at the driver logs for that.
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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.