Databricks-GenAI-Assoc Application Development Practice Question
Exhibit
import mlflow
mlflow.set_tracking_uri("databricks")
# Missing configuration here
model_info = mlflow.pyfunc.log_model(artifact_path="llm_model", python_model=model)Refer to the exhibit. The developer is attempting to log a custom model to the Unity Catalog. Which configuration is missing to ensure the model is registered correctly under the specified Unity Catalog location?
⚠ Common exam trap
Candidates often assume the default MLflow registry URI automatically points to Unity Catalog, failing to realize that an explicit set_registry_uri('databricks-uc') call is required to shift from the workspace registry.
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
✓
mlflow.set_registry_uri("databricks-uc")
To log a model to Unity Catalog, the registry must be specified as 'databricks-uc'. The default behavior for 'databricks' tracking URI is to log to the workspace model registry. By explicitly setting the registry to the Unity Catalog destination, the developer ensures the model is governed by the correct security and lineage frameworks, allowing for seamless downstream deployment and access control within the enterprise 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.
- ✗
mlflow.set_registry_uri("databricks")
Why it's wrong here
Setting the registry URI to 'databricks' targets the legacy workspace-based model registry, not Unity Catalog. This is the incorrect URI for modern Databricks model management, as it lacks the advanced lineage and governance features provided by the Unity Catalog infrastructure required for modern AI applications.
- ✓
mlflow.set_registry_uri("databricks-uc")
Why this is correct
The 'databricks-uc' registry URI tells MLflow to route the model registration process to Unity Catalog. This is the mandatory configuration for using Unity Catalog's model registry features, which are required for cross-workspace model sharing and centralized governance, ensuring the model artifact is properly stored and discoverable.
- ✗
mlflow.set_tracking_uri("databricks-uc")
Why it's wrong here
The tracking URI is used to define where experiment data is logged, while the registry URI defines where model artifacts are stored. Mixing these up or using the wrong URI will lead to failure in the model registration process, preventing the model from being saved in the desired location.
- ✗
mlflow.set_experiment("uc_experiment")
Why it's wrong here
Setting an experiment name only organizes the run logs in the UI; it does not dictate the destination of the model registry. The registry destination is a separate configuration that must be explicitly set to ensure the model artifact is stored within the Unity Catalog hierarchy, not the workspace.
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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-GenAI-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-GenAI-Assoc exam.