Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is building a recurring batch inference pipeline in Databricks. The model is registered in Unity Catalog as a model version and must always use the version currently tagged as 'champion', which is reassigned after each retraining run. The engineer wants the inference notebook to resolve this alias at runtime rather than hard-coding a version number. Which approach should the engineer use in the notebook?
⚠ Common exam trap
The trap here is assuming that MLflow model stages like Production still apply inside Unity Catalog, when Unity Catalog models rely on aliases instead.
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
✓
Load the model with mlflow.pyfunc.load_model("models:/catalog.schema.model_name@champion") to resolve the alias at runtime.
Using a model alias in the models:/ URI lets the pipeline resolve the promoted version at runtime, so retraining and promotion do not require editing the inference notebook. The alias is a mutable pointer managed in Unity Catalog, and mlflow.pyfunc.load_model understands the models:/ scheme with the @alias suffix, making it the intended mechanism for champion-based batch scoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Load the model with mlflow.pyfunc.load_model("models:/catalog.schema.model_name@champion") to resolve the alias at runtime.
Why this is correct
The models:/ URI with the @champion suffix resolves the Unity Catalog model alias to the currently assigned version at load time, so the notebook always picks up the latest promoted model without edits. This is the documented pattern for alias-based deployment in Databricks, and it works with mlflow.pyfunc.load_model as well as scoring inside the notebook or a job task.
- ✗
Load the model with mlflow.pyfunc.load_model("models:/catalog.schema.model_name/Production") to resolve the stage at runtime.
Why it's wrong here
Model stages such as Production are part of the legacy MLflow Model Registry and are not supported for models registered in Unity Catalog. Unity Catalog models use aliases instead of stages, so this URI would fail or point at an invalid target. The scenario explicitly registers the model in Unity Catalog, so a stage-based URI cannot resolve the promoted version.
- ✗
Read the model version number from the MLflow Tracking API at runtime and pass it to mlflow.pyfunc.load_model with a numeric version URI.
Why it's wrong here
Querying the Tracking API for a version number and then loading by numeric version adds an unnecessary lookup step and still requires the notebook to know which version is promoted. A model alias already encapsulates that mapping, so this approach duplicates functionality and increases the chance of loading a stale or wrong version if the lookup logic is incorrect.
- ✗
Download the model artifacts from the Unity Catalog volume path with dbutils.fs.cp and load them with mlflow.pyfunc.load_model using the local path.
Why it's wrong here
Copying artifacts from the underlying storage path bypasses the model registry entirely and does not track which version is promoted as champion. It also risks loading an inconsistent set of files if the copy is partial, and it provides no alias resolution. The registry URI is the supported way to load a specific registered version by alias.
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-ML-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-ML-Assoc exam.