Databricks-DE-Assoc Implementing CI/CD Practice Question
A team wants to automate deployment of Databricks jobs from a Git repository using a CI/CD pipeline. They need a tool that reads a declarative project definition and creates or updates jobs, pipelines, and notebooks in a target workspace. Which Databricks capability should they use?
⚠ Common exam trap
Many candidates confuse Databricks Repos, which syncs files for interactive work, with Asset Bundles, which deploy resource definitions declaratively.
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
✓
Databricks Asset Bundles, using databricks bundle deploy to apply the project definition to the target workspace.
Databricks Asset Bundles are the declarative deployment mechanism for Databricks resources. A databricks.yml file plus source files defines jobs, pipelines, and notebooks, and databricks bundle deploy applies that definition to a target workspace. Repos handle file sync for development, SQL warehouse APIs run queries, and cluster policies govern compute; none of them deploys resource definitions from Git.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cluster policies, by attaching a policy that references the Git repository and applies job configurations at cluster start.
Why it's wrong here
Cluster policies constrain how clusters can be configured; they do not deploy jobs or pipelines. A policy cannot read a Git repository or create resources. While policies are useful for governance in a CI/CD context, they are not a deployment mechanism, so this approach does not meet the requirement to create or update jobs and pipelines automatically.
- ✓
Databricks Asset Bundles, using databricks bundle deploy to apply the project definition to the target workspace.
Why this is correct
Databricks Asset Bundles let you define jobs, pipelines, and notebooks declaratively in databricks.yml along with the source files. Running databricks bundle deploy reads that definition and creates or updates the corresponding resources in the target workspace. This is the supported mechanism for automated, repeatable deployment from a Git repository in a CI/CD pipeline.
- ✗
Databricks Repos, by adding the repository and clicking Sync in the target workspace before each release.
Why it's wrong here
Databricks Repos provides Git integration for interactive development, but syncing a repository is a manual or scripted step that does not create or update job and pipeline resources declaratively. It manages files, not resource definitions. Using it as the deployment mechanism leaves resource creation to manual configuration and does not provide the repeatable, declarative deployment the team needs.
- ✗
The Databricks SQL warehouse API, using scheduled queries to create jobs and pipelines in the target workspace.
Why it's wrong here
The SQL warehouse API executes SQL statements against data, not deployment operations. It has no capability to create or update jobs, pipelines, or notebook resources. Attempting to use it for deployment misunderstands its purpose and would not produce the declarative resource management the team requires from their pipeline.
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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.