Databricks-DE-Assoc Implementing CI/CD Practice Question
A team uses Databricks Asset Bundles to define a job that must exist in both a staging and a production workspace with different cluster sizes. They want a single bundle definition that deploys correctly to both targets. Which configuration should they use?
⚠ Common exam trap
The trap here is assuming cluster policies or shared workspace links can substitute for target-specific bundle overrides.
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
✓
Use a single bundle with `targets` for staging and production, and override cluster settings per target using target-specific variables or overrides in `databricks.yml`.
Databricks Asset Bundles are designed for multi-environment deployment. A single `databricks.yml` with `targets` for staging and production, combined with target-specific overrides for cluster configuration, lets one bundle definition produce environment-appropriate resources without duplicating the job definition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Define two separate bundles, one per workspace, and deploy each with its own `databricks bundle deploy` command.
Why it's wrong here
Maintaining two separate bundles duplicates the job definition and defeats the purpose of a single source of truth. Changes must be applied twice, increasing drift risk, whereas Databricks Asset Bundles support multiple targets within one bundle precisely to avoid this duplication.
- ✓
Use a single bundle with `targets` for staging and production, and override cluster settings per target using target-specific variables or overrides in `databricks.yml`.
Why this is correct
Databricks Asset Bundles allow multiple `targets` in one `databricks.yml`, each with its own workspace host and overrides. Cluster sizes can differ per target through target-scoped variables or resource overrides, so one bundle definition deploys correctly to both staging and production.
- ✗
Hard-code the production cluster size in the bundle and use a cluster policy in staging to downsize it at runtime.
Why it's wrong here
Cluster policies constrain what users can configure, but they do not rewrite a job's requested cluster size. Hard-coding production values means staging runs with production-sized clusters unless a policy blocks them, which is fragile and does not achieve the intended per-environment sizing.
- ✗
Deploy the bundle only to production and point the staging workspace at the production job through a shared workspace link.
Why it's wrong here
Databricks does not provide a shared workspace link that lets one workspace run another workspace's jobs natively. Staging would not have its own isolated job, and deployments would affect production directly, so this approach fails the requirement for separate environments.
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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.