Databricks-ML-Pro Model Development Practice Question
Exhibit
MLflow config: mlflow.set_tracking_uri('databricks')
model_uri = 'models:/MyModel/1'
mlflow.pyfunc.load_model(model_uri)Refer to the exhibit. You are loading a model from the registry. What does the 'models:/MyModel/1' URI specifically represent?
⚠ Common exam trap
Candidates confuse run IDs with model URIs, often assuming the URI points to a temporary tracking run artifact rather than a versioned registry model.
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
✓
A unique identifier for the registered model name and its specific version.
The URI format 'models:/ModelName/Version' is the standard way to reference registered models within MLflow. It points to a specific, versioned artifact stored in the registry, ensuring that the inference code consistently uses the exact model state that was approved. This abstraction allows developers to change model versions without modifying the underlying inference application, promoting a stable production environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A direct pointer to the local file system path of the model artifact.
Why it's wrong here
The 'models:/' prefix is a special protocol identifier for the MLflow Model Registry, not a local file system path. It instructs MLflow to query the registry service to locate and fetch the model, rather than attempting to read from a directory on the local machine where the code executes.
- ✓
A unique identifier for the registered model name and its specific version.
Why this is correct
This URI is a versioned reference that maps to a specific, immutable artifact in the Model Registry. Using this format ensures that the loading process retrieves the correct, validated model version, which is critical for maintaining consistency and reliability in downstream inference applications and automated model serving pipelines.
- ✗
An alias for the most recently trained model in the current experiment.
Why it's wrong here
This is a fixed reference to a version number, not a pointer to the 'most recent' model. The most recent model would be accessed via a different convention, such as using 'latest' or by querying the experiment directly, and this specific URI does not dynamically update when new runs occur.
- ✗
A reference to the raw source code used to train the model.
Why it's wrong here
The model URI points to the serialized model artifact and its associated metadata, not the raw training code. While the artifact might contain information about the code, the URI itself is an access mechanism for the model object used for inference, not a repository for source code files.
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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
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