Databricks-ML-Pro Model Deployment Practice Question
When using the Unity Catalog Model Registry, what are the primary advantages of using 'Aliases' over 'Versions' when calling a model from a production application? (Select TWO)
⚠ Common exam trap
Test-takers often recommend hardcoding specific integer version numbers in production applications, leading to brittle codebases that require manual code deployments for every model update.
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
✓
Aliases allow the application to always point to a stable name like '@prod' instead of a hardcoded version number.
Aliases provide a layer of abstraction between the application code and the specific model version. This decoupling is a cornerstone of MLOps, as it allows for seamless updates and rollbacks without modifying the client-side code that consumes the model. It also improves readability by using descriptive names like '@champion'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Aliases allow the application to always point to a stable name like '@prod' instead of a hardcoded version number.
Why this is correct
By using a symbolic name, the engineering team can update the model version that the alias points to in the registry. The production application, which calls the alias, will automatically start receiving predictions from the new version without requiring a code redeployment or restart.
- ✗
Aliases improve performance by caching the model weights on the client side.
Why it's wrong here
Aliases are a management and routing feature, not a performance or caching mechanism. The model weights are still managed and loaded by the Databricks Serving infrastructure; the alias simply tells the infrastructure which specific version's weights should be used to process a given request.
- ✓
Aliases allow for easier rollbacks by simply reassigning the alias to a previous version.
Why this is correct
If a newly deployed 'champion' model performs poorly, an administrator can instantly point the '@champion' alias back to the previous version. This action takes effect immediately across all services using that alias, providing a rapid and safe way to mitigate production issues.
- ✗
Aliases are required to enable GPU acceleration for models in Unity Catalog.
Why it's wrong here
Hardware acceleration is a property of the serving endpoint's compute configuration, not the registry's naming convention. Both versions and aliases can be served on any supported hardware type; the alias is purely for organizational and deployment workflow purposes.
- ✗
Only models with an alias can be used in Spark structured streaming jobs.
Why it's wrong here
Spark structured streaming can load models using specific version numbers, stages (in the legacy registry), or aliases. While aliases are recommended for production stability, they are not a technical requirement for the Spark engine to load and apply a model to a stream.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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