Databricks-DE-Assoc Implementing CI/CD Practice Question
A data engineering team is setting up a CI/CD pipeline for Databricks notebooks using Databricks Repos and a Git provider. They want to ensure that changes are tested before being merged and that production deployments are controlled. Which TWO practices should they implement? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse continuous synchronization of the main branch with proper CI/CD, when production should instead be updated through a controlled release step.
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
✓
Run automated tests in a CI job on a feature branch using a dedicated test workspace before merging to main.
A robust CI/CD process for Databricks Repos runs automated tests on feature branches in a dedicated test workspace, then gates merges on CI success. Production deployment should be a separate, controlled step that updates the production Repo to a tagged release, providing an immutable and rollback-friendly reference. These two practices together enforce quality gates and controlled promotion, which are core to CI/CD with Databricks Repos.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store production credentials in the Repo's notebooks as widgets so the CI job can read them during tests.
Why it's wrong here
Embedding credentials in notebooks exposes secrets in Git history and to anyone with Repo read access. Secrets management should use Databricks secret scopes or the CI provider's secret store, not widgets or source files. Widgets are also visible in notebook context and are not designed for secure credential storage.
- ✓
Run automated tests in a CI job on a feature branch using a dedicated test workspace before merging to main.
Why this is correct
Running tests on feature branches in an isolated test workspace validates changes without affecting production data or users. This practice catches integration issues early and aligns with the CI principle of fast feedback. Using a dedicated workspace also prevents accidental writes to production catalogs and keeps test credentials separate from production credentials.
- ✓
Merge changes only after the CI pipeline passes and use a separate deployment job to update the production Repo to a tagged release.
Why this is correct
Gating merges on CI success and deploying production from a tagged release gives a clear, auditable promotion path. Tags provide immutable references that can be rolled back if needed, and a separate deployment job separates build from release concerns. This matches the recommended pattern of using Repos with branch protection and controlled production updates.
- ✗
Allow developers to commit directly to the main branch in the production workspace to reduce merge conflicts.
Why it's wrong here
Direct commits to main in production eliminate peer review and CI validation, increasing the risk of broken pipelines and data incidents. Merge conflicts should be resolved in feature branches before review, not by bypassing branch protection. This practice also violates separation of duties, which auditors expect in regulated environments.
- ✗
Configure the production Repo to track the main branch and enable automatic pull on every commit.
Why it's wrong here
Automatic pull on every commit to main bypasses review and testing gates, allowing unreviewed code to run in production. CI/CD best practice requires a controlled promotion step, often via a release branch or tag, not continuous automatic sync. This option also removes the ability to roll back predictably because production would always reflect the latest main commit.
Visual reference
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.