Databricks-ML-Pro ML Ops Practice Question
You are implementing a CI/CD pipeline for a Databricks ML project using Databricks Repos and Databricks Asset Bundles. You need to ensure that the pipeline promotes code and model artifacts across dev, staging, and prod workspaces consistently. Which TWO practices should you implement to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is assuming that using identical catalog names or hardcoding workspace details simplifies multi-environment deployment, when it actually breaks isolation and portability.
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
✓
Register model versions to a shared Unity Catalog metastore that is accessible from all workspaces, using environment-specific model names or aliases.
Using Databricks Asset Bundles with target blocks and variables, and registering models to a shared Unity Catalog metastore with environment-specific names or aliases, together provide a consistent, governed promotion path. These practices externalize configuration and centralize artifact management, which are core to reliable CI/CD across multiple workspaces.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Register model versions to a shared Unity Catalog metastore that is accessible from all workspaces, using environment-specific model names or aliases.
Why this is correct
A shared Unity Catalog metastore allows model versions to be registered once and referenced across workspaces. By using environment-specific model names or aliases (e.g., `model_dev`, `model_prod`), you maintain separation while enabling promotion. This aligns with Databricks' recommended governance model and ensures artifacts are consistently available to all environments.
- ✓
Store workspace-specific configuration in `databricks.yml` target blocks and use bundle variables for environment differences.
Why this is correct
Databricks Asset Bundles use a `databricks.yml` file with target blocks to define per-environment settings such as workspace host, cluster IDs, and job parameters. Bundle variables allow parameterization of values that differ across environments. This practice ensures the same bundle can be deployed to dev, staging, and prod without code changes, which is essential for consistent CI/CD promotion.
- ✗
Store model artifacts in a separate cloud storage bucket outside Databricks and copy them manually between environments.
Why it's wrong here
Manual copying of artifacts outside Databricks introduces human error, lacks versioning, and bypasses Unity Catalog governance. It also complicates lineage and reproducibility. The recommended approach is to use Unity Catalog for model registration and Databricks Asset Bundles for deployment, not external buckets with manual steps.
- ✗
Use the same Unity Catalog catalog name in all workspaces and rely on workspace-local paths for artifacts.
Why it's wrong here
Using identical catalog names and workspace-local paths breaks isolation between environments and can cause accidental overwrites. Best practice is to use distinct catalogs or schemas per environment and reference them via bundle variables. Workspace-local paths are not portable across workspaces, defeating the purpose of a consistent promotion pipeline.
- ✗
Hardcode the production workspace URL and cluster ID in the notebook code to simplify deployment.
Why it's wrong here
Hardcoding workspace-specific values makes the code non-portable and error-prone. It prevents the same code from running in dev or staging without modification, and it exposes sensitive infrastructure details. CI/CD pipelines should externalize such configuration, not embed it in notebooks, to maintain flexibility and security.
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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-ML-Pro 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-ML-Pro exam.