Databricks-GenAI-Assoc Assembling and Deploying Apps Practice Question
A team is preparing a Databricks Asset Bundle that deploys a GenAI application consisting of a job that builds a vector index and a Model Serving endpoint that hosts the agent. Before merging, they want the pipeline to validate and deploy the bundle to a staging workspace automatically. Which TWO bundle capabilities should the pipeline rely on? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse bundle run, which executes deployed resources, with bundle deploy, which publishes the bundle definition to a target workspace.
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 databricks bundle deploy with the staging target to create or update the job, endpoint, and related resources in the staging workspace.
A bundle pipeline validates the configuration against the target workspace and then deploys it to that target. Validation catches malformed or unresolvable configuration before anything changes, and deployment applies the resolved resources to the staging workspace, which together form the automated promotion path the team wants.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Run databricks bundle deploy with the staging target to create or update the job, endpoint, and related resources in the staging workspace.
Why this is correct
bundle deploy applies the resolved configuration to the selected target, creating or updating the declared resources and recording the deployment state. Pointing it at the staging target is how the pipeline promotes the bundle into that workspace.
- ✗
Run databricks bundle schema to regenerate the JSON schema and commit it to the repository.
Why it's wrong here
bundle schema outputs the JSON schema describing valid bundle configuration, useful for editor tooling, but it neither validates a specific bundle against a workspace nor deploys anything. It does not gate a merge or place resources in staging.
- ✗
Run databricks bundle run to execute the vector index job as part of the merge validation.
Why it's wrong here
bundle run triggers a resource such as a job or pipeline, which executes application logic rather than validating or deploying the bundle definition. Running the index build on every merge would consume compute and is not the deployment mechanism the pipeline needs.
- ✗
Run databricks bundle generate to create resource definitions from existing workspace objects before each deployment.
Why it's wrong here
bundle generate pulls existing workspace objects into bundle resource definitions, which is a migration aid rather than a deployment step. Running it during CI would risk overwriting the authored configuration with whatever currently exists in the workspace.
- ✓
Run databricks bundle validate to check the bundle configuration against the target workspace before deployment.
Why this is correct
bundle validate parses the configuration, resolves variables for the specified target, and confirms the resources are well formed against the workspace, catching schema and reference errors before any deployment occurs. It is the standard pre-deployment gate in a CI pipeline.
Visual reference
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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-GenAI-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-GenAI-Assoc exam.