Databricks-DE-Assoc Implementing CI/CD Practice Question
When promoting code from a development workspace to a production workspace, what is the primary risk of using manual notebook exports?
⚠ Common exam trap
Candidates often view manual notebook exports as harmless, overlooking how easily they introduce code drift and lack metadata synchronization across environments.
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
✓
The process creates an inconsistent state between development and production.
Manual notebook exports lack version integrity and metadata synchronization, leading to 'code drift' where the production code doesn't match the source of truth in Git. This manual process is prone to human error, such as importing the wrong version or missing library dependencies. Standardizing on Git-integrated workflows or Databricks Asset Bundles mitigates this risk by automating the deployment and ensuring that exactly what was tested in development reaches production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The notebooks will run significantly slower in the production environment.
Why it's wrong here
Notebook execution speed is determined by cluster configuration and data volume, not the method of deployment. While manual deployment is risky for governance and consistency, it does not inherently cause performance degradation in the underlying Spark engine or the notebook's logic during runtime execution.
- ✓
The process creates an inconsistent state between development and production.
Why this is correct
Manual processes lack the rigor of automated CI/CD pipelines. Differences in libraries, environment settings, or notebook versions often occur when files are manually copied. These inconsistencies lead to code that works in development but fails in production, causing difficult-to-debug errors and potential downtime for critical data tasks.
- ✗
The Databricks workspace will automatically delete the old notebooks.
Why it's wrong here
Databricks does not automatically delete existing notebooks during a manual import. If the file names overlap, the system will either overwrite the file or prompt the user, depending on the tool used. The risk lies in the lack of version control, not in the automatic deletion of assets.
- ✗
Manual exports are prohibited by Databricks security policies.
Why it's wrong here
While manual exports are discouraged due to CI/CD best practices, they are not strictly prohibited by Databricks platform settings. The issue is operational risk, not a violation of platform security constraints. Organizations should focus on enforcing automated CI/CD patterns rather than relying on manual file handling.
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-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.