Data Engineer Associate · 10% of the exam

Implementing CI/CD: free practice questions

5 sample questions from our 5-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real Databricks exam.

1. 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 automatically trains ML models
  • C. It removes the need for any testing
  • 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 (B), remove testing (C), or store output data (D).

2. 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.

3. 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. The VACUUM command
  • D. A BI dashboard export
Explanation

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

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

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

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

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. Hard-code the prod catalog everywhere
  • C. Maintain two entirely separate, manually edited codebases
  • D. Disable Unity Catalog
Explanation

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

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Implementing CI/CD — Free Data Engineer Associate Practice Questions | DataCertPrep — Certification Prep