Databricks Data Engineer Associate · 10% practice allocation

Databricks Data Engineer Associate: Implementing CI/CD Practice Questions

5 free sample questions from the Implementing CI/CD domain, with answers and explanations. The full Data Engineer Associate bank has 300 questions across 7 domains. Questions are scenario-based and written from the official Databricks exam guide.

1.

Which Databricks feature packages notebooks, jobs, and pipeline definitions as code so a project can be version-controlled and deployed consistently across environments?

  • A. Databricks Asset Bundles (DABs)✓ Correct
  • B. A single shared notebook copied by hand
  • C. VACUUM
  • D. Delta Sharing
Explanation

Databricks Asset Bundles define resources (jobs, pipelines, notebooks) as code in YAML for consistent, repeatable deployment across environments. Hand-copying (B) isn't CI/CD, and VACUUM (C) and Delta Sharing (D) are unrelated.

2.

Which tool is commonly used to automate deploying Databricks Asset Bundles from a CI pipeline?

  • A. The Databricks CLI (databricks bundle deploy)✓ Correct
  • B. A spreadsheet macro
  • C. A BI dashboard export
  • D. The VACUUM command
Explanation

The Databricks CLI (e.g., databricks bundle validate/deploy) automates bundle deployment from CI systems. The other options don't deploy code.

3.

How should code be promoted from development to production in a CI/CD workflow on Databricks?

  • A. Copy-paste cells between workspaces manually
  • B. Keep only one copy of the code with no history
  • C. Store code in Git (via Repos) and deploy it to each environment through an automated pipeline✓ Correct
  • D. Edit production notebooks directly with no version control
Explanation

Git-based source control with automated deployment promotes tested code across dev/staging/prod reliably. Editing prod directly (D), manual copy-paste (A), and no versioning (B) are error-prone anti-patterns.

4.

What is a key benefit of managing Databricks notebooks in Git-backed Repos for CI/CD?

  • A. Version history, code review, and branch-based collaboration before changes reach production✓ Correct
  • B. It removes the need for any testing
  • C. It automatically trains ML models
  • D. It stores the pipeline's output data
Explanation

Git-backed Repos give version history, pull-request review, and branching so changes are reviewed before release. It doesn't train models (C), remove testing (B), or store output data (D).

5.

The same job must point to a dev catalog in development and a prod catalog in production without changing code. What is the right approach?

  • A. Parameterize the environment (e.g., bundle variables/targets or job parameters) so values differ per environment✓ Correct
  • B. Disable Unity Catalog
  • C. Hard-code the prod catalog everywhere
  • D. Maintain two entirely separate, manually edited codebases
Explanation

Parameterizing per-environment values (via bundle targets/variables or job parameters) keeps one codebase deployable to any environment. Hard-coding (C) and duplicate codebases (D) are anti-patterns, and disabling UC (B) is unrelated.

Test yourself across every Data Engineer Associate domain

The free Data Engineer Associate practice test mixes about 20 questions weighted like the exam. The full 300-question bank adds instant grading, flashcards and a timed mock exam.

Take the free Data Engineer Associate practice testPractice the full bank

Study this domain

Implementing CI/CD study guide (Data Engineer Associate) →

Other Data Engineer Associate domains

← All Databricks Data Engineer Associate study resources