Databricks-ML-Assoc Model Deployment Practice Question
A data scientist registers a scikit-learn model in the Unity Catalog model registry with the name `prod.ml_team.fraud_detector`. They now want to serve it with Databricks Model Serving. Which value should be supplied as the model identifier when creating the serving endpoint?
⚠ Common exam trap
The trap here is assuming that MLflow stage-based URIs like models:/name/Production still work for models registered in Unity Catalog, when Unity Catalog models are addressed by their three-level name and use aliases instead of stages.
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
✓
prod.ml_team.fraud_detector
Because the model was registered in Unity Catalog, the endpoint must be created from the three-level namespace catalog.schema.model_name, optionally pinned to a version. Stage-based URIs and raw storage or run paths do not identify a Unity Catalog registered model, so they cannot be used as the served entity. The fully qualified name is the correct identifier.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
prod.ml_team.fraud_detector
Why this is correct
Unity Catalog models are referenced by their fully qualified three-level name, catalog.schema.model_name, so prod.ml_team.fraud_detector is exactly the identifier Model Serving expects. Because the model lives in Unity Catalog rather than the workspace registry, the endpoint resolves the model version directly from that namespace, and permissions on the catalog, schema, and model govern who can serve it.
- ✗
models:/prod.ml_team.fraud_detector/Production
Why it's wrong here
The models:/ URI scheme with a stage such as Production belongs to the MLflow Model Registry stage workflow. Unity Catalog models do not use stages; they use aliases and versions instead. Supplying a stage-based URI for a Unity Catalog model will not resolve, because stage transitions are not supported for models registered in Unity Catalog.
- ✗
dbfs:/databricks/mlflow/prod/ml_team/fraud_detector
Why it's wrong here
A dbfs:/ path is a raw storage location, not a registered model identifier. Even if the serialized model files happen to live under a DBFS path, Model Serving requires a registry reference so it can resolve a version, apply permissions, and track lineage. Pointing at storage directly bypasses the registry and is not a supported endpoint source.
- ✗
runs:/<run_id>/model
Why it's wrong here
A runs:/ URI points at an artifact inside a specific MLflow run, which is useful for ad hoc loading, not for serving a registered model. Model Serving endpoints are built from registered model versions so they can be versioned, permissioned, and rolled back. A run artifact path also would not carry Unity Catalog governance or model-lineage metadata.
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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
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