Databricks-DE-Assoc Implementing CI/CD Practice Question
Your team is migrating a manual Databricks job to a CI/CD pipeline. You need to ensure the job configuration is version-controlled and deployed programmatically. Which approach aligns with Databricks best practices?
⚠ Common exam trap
Candidates often choose manual UI deployment or legacy JSON templates instead of modern Databricks Asset Bundles (DABs) combined with YAML configuration files, which are the current industry best practice for programmatic CI/CD pipelines.
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
✓
Define the job configuration in YAML files using Databricks Asset Bundles and deploy via the CLI.
Using Databricks Asset Bundles (DABs) is the recommended approach for modern CI/CD. It treats infrastructure as code, allowing you to define jobs, pipelines, and workflows in YAML files. This ensures consistency across environments like development, staging, and production. By decoupling the job definition from the workspace UI, you gain auditability, reproducibility, and the ability to integrate seamlessly with Git workflows, reducing manual configuration errors and accelerating release cycles for data engineering teams.
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 export the job JSON via the UI and copy-paste it into the production workspace.
Why it's wrong here
Manual exports lead to configuration drift and human error. This process is not repeatable or automated, making it unsuitable for a CI/CD pipeline. Databricks recommends programmatic deployment to ensure the production environment exactly matches the version-controlled definition stored in your repository without manual intervention.
- ✗
Use the Databricks UI to update production jobs directly to ensure changes are applied immediately.
Why it's wrong here
Updating production jobs directly in the UI bypasses version control and peer review processes. This practice violates CI/CD principles, as there is no audit trail or ability to roll back changes. Changes must move through testing environments before reaching production, ensuring stability and reliability of data workloads.
- ✓
Define the job configuration in YAML files using Databricks Asset Bundles and deploy via the CLI.
Why this is correct
Databricks Asset Bundles allow you to define jobs as code in YAML, which can be stored in Git. Using the Databricks CLI to deploy these files enables automated, consistent, and repeatable deployments across different environments, which is the cornerstone of robust CI/CD practices in the Databricks platform.
- ✗
Write a custom Python script that uses the workspace API to recreate the job from scratch every time.
Why it's wrong here
While possible, custom scripts are difficult to maintain and lack the native lifecycle management features provided by Databricks Asset Bundles. Using official tooling reduces technical debt and ensures compatibility with platform updates, whereas custom scripts require significant effort to handle edge cases, error states, and state management.
Visual reference
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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.