Databricks-DE-Pro Debugging and Deploying Practice Question
A data engineer is preparing to deploy a production Databricks Workflow that must be maintainable and auditable. The engineer wants to ensure that changes to the workflow are tracked and that failures can be diagnosed quickly. Which TWO practices should the engineer implement? (Choose two.)
⚠ Common exam trap
The trap here is choosing notification or manual documentation practices that seem helpful but do not provide the version control and durable logging needed for maintainability and auditing.
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
✓
Store the workflow definition in a Git repository and deploy using Databricks Asset Bundles
Version-controlling workflow definitions in Git and deploying with Databricks Asset Bundles ensures changes are tracked and reproducible. Delivering job logs to cloud storage retains diagnostic data for troubleshooting and auditing. Together, these practices provide the maintainability and auditability required for production workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually document each deployment in a shared spreadsheet
Why it's wrong here
Manual documentation is error-prone and does not integrate with deployment automation or provide a reliable audit trail. It cannot enforce consistency between code and deployment. Automated version control and log retention are superior practices for maintainability and auditability, making manual spreadsheets inadequate for production workflows.
- ✗
Use an all-purpose cluster instead of a job cluster for the workflow
Why it's wrong here
All-purpose clusters are intended for interactive analysis and are more expensive and less isolated than job clusters. They do not inherently improve maintainability or auditability of workflow definitions. Production workflows should use job clusters for cost efficiency and isolation, while version control and logging address the stated requirements.
- ✗
Configure email notifications for job success and failure
Why it's wrong here
Email notifications alert recipients to job status but do not provide version control or detailed diagnostic information. They are useful for awareness but insufficient for auditing changes or deep debugging. The engineer should focus on versioned deployment and centralized logging to meet the stated requirements.
- ✓
Store the workflow definition in a Git repository and deploy using Databricks Asset Bundles
Why this is correct
Version-controlling workflow definitions in Git and deploying via Databricks Asset Bundles provides change tracking, code review, and reproducible deployments. This practice ensures that every modification is auditable and that the deployed workflow matches the reviewed source, which is essential for maintainability and compliance in production environments.
- ✓
Enable job run logs to be delivered to a cloud storage location for long-term retention
Why this is correct
Delivering job logs to cloud storage ensures that diagnostic data is retained beyond the default retention period and is accessible for post-mortem analysis. This supports quick diagnosis of failures and provides an audit trail of job executions, complementing version-controlled deployment for maintainability.
About these practice questions
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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-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.