Databricks-ML-Pro ML Ops Practice Question
A data scientist has trained a scikit-learn model and logged it with MLflow. They now want to register this model in the MLflow Model Registry and transition it to 'Production' to be served via Databricks Model Serving. Which of the following is a prerequisite for registering the model?
⚠ Common exam trap
The trap here is assuming that a model signature or admin approval is needed to register a model, when actually only a valid MLflow run is required.
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
✓
The model must be logged using the MLflow Tracking API and have a valid run ID.
Registering a model in MLflow Model Registry requires that the model artifact is linked to an MLflow run. The run ID establishes the model's origin and enables versioning. Other options are either optional or incorrect; signatures, experiment associations, and admin approvals are not required for registration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model must be logged using the MLflow Tracking API and have a valid run ID.
Why this is correct
To register a model, you need a model artifact that is associated with a run in MLflow Tracking. The run ID provides the lineage and allows the registry to reference the model's source. Without a valid run, registration cannot proceed.
- ✗
The model must be logged to an MLflow experiment that is associated with a registered model.
Why it's wrong here
MLflow experiments and registered models are separate concepts. You can log a model to any experiment and then register it under a new or existing registered model name. There is no requirement for the experiment to be pre-associated with a registered model.
- ✗
The model must be logged with a signature.
Why it's wrong here
While a signature is recommended for serving, it is not a prerequisite for registering a model in the MLflow Model Registry. You can register a model without a signature, but serving it may require additional configuration. The registry accepts models regardless of signature presence.
- ✗
The model must be approved by a workspace admin.
Why it's wrong here
MLflow Model Registry does not require admin approval for registration. Any user with write permissions to the registry can register a model. Approval workflows can be implemented using stage transitions or webhooks, but they are not prerequisites for registration.
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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-Pro 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-Pro exam.