Databricks-DE-Pro Debugging and Deploying Practice Question
A data engineer is investigating a job failure that occurred only in the production environment. Which TWO features in Databricks help in comparing the production environment to the development environment?
⚠ Common exam trap
Candidates often suggest manual code comparison or running both jobs simultaneously. They fail to see that configuration parity is best verified via the Jobs UI export and standardized CI/CD version control.
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
✓
Use the 'View as JSON' feature in the Jobs UI to compare job configurations.
Comparing environment configurations is essential for troubleshooting parity issues. Databricks provides tools like the Jobs UI for export and version control integration for code, which help engineers identify subtle differences between environments. Ensuring parity is critical for reducing 'it works on my machine' scenarios. By utilizing these tools, engineers can verify cluster settings, library versions, and code versions to isolate why a process fails in production but succeeds in development.
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 the 'View as JSON' feature in the Jobs UI to compare job configurations.
Why this is correct
Exporting and comparing job JSON configurations is an effective way to identify discrepancies in parameters, cluster sizes, or timeout settings. This allows engineers to systematically check for configuration drift between the development workspace and the production workspace, which is a common source of environment-specific failures.
- ✗
Use the 'Cluster Logs' to compare the OS kernel versions of the nodes.
Why it's wrong here
Comparing OS kernel versions is rarely the solution for job failures, as Databricks manages the underlying runtime environment. If there is a disparity, it would be due to selecting different Databricks Runtime versions, not the specific OS kernel, which is abstracted away from the end user.
- ✓
Review the git branch history and configuration files in the CI/CD pipeline.
Why this is correct
Checking the CI/CD pipeline ensures that the exact code and configuration being deployed to production matches what was tested in development. This identifies issues where different branches were accidentally merged or where environment-specific variables were incorrectly injected into the production job, causing unexpected runtime behavior.
- ✗
Enable the 'Debug Mode' on the Spark Driver to see raw system calls.
Why it's wrong here
Debug mode for raw system calls is not a standard feature in Databricks and would not provide useful context for comparing environment configurations. This level of debugging is excessively low-level and does not address the high-level configuration differences that typically cause environment-specific pipeline failures.
- ✗
Query the 'workspace_users' table to see who ran the job last.
Why it's wrong here
Knowing who ran a job last does not help identify configuration differences. While auditing is useful for security, the technical investigation of why a job fails requires comparing settings, libraries, and code, not the identity of the user who triggered the last execution.
Quick reference
RAID Level Comparison
| RAID Level | Min Disks | Fault Tolerance | Read | Write | Usable Capacity |
|---|---|---|---|---|---|
| RAID 0 | 2 | None | Excellent | Excellent | 100% |
| RAID 1 | 2 | 1 disk | Good | Moderate | 50% |
| RAID 5 | 3 | 1 disk | Good | Moderate | 67–94% |
| RAID 6 | 4 | 2 disks | Good | Lower | 50–88% |
| RAID 10 | 4 | 1 disk per mirror | Excellent | Good | 50% |
RAID is not a backup strategy — it protects against disk failure but not against accidental deletion, ransomware, or site-level events.
About these practice questions
This Databricks-DE-Pro question is part of Courseiva's 267-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. 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-Pro 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-Pro exam.