Databricks-ML-Assoc Model Development Practice Question
A data scientist trains a model with MLflow on Databricks and logs it using mlflow.sklearn.log_model with a registered_model_name. A downstream batch job loads the model by stage using models:/<name>/Staging. Weeks later, a colleague promotes a new version to Staging and the batch job's predictions change without any code deployment. Which change best prevents unintended downstream consumption while keeping promotion workflows intact?
⚠ Common exam trap
The trap here is believing that a model signature or archiving old versions freezes what a stage-based URI returns, when only an explicit version reference is immutable.
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 by explicit version URI instead of by stage.
Stage-based URIs are mutable pointers: whatever version is transitioned into a stage becomes the artifact that stage references. Pinning a consumer to an explicit model version makes its dependency immutable, so later promotions affect only consumers that intentionally follow stages. This preserves Registry-based promotion while eliminating silent behaviour changes in the batch job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Archive the previous versions in the Model Registry before each promotion.
Why it's wrong here
Archiving old versions changes their lifecycle status but does not stop stage-based URIs from resolving to the newly promoted version. The batch job would still follow the Staging pointer to the latest promoted artifact. Archiving is useful for cleanup and governance, yet it does not provide the immutability the scenario requires for the downstream consumer.
- ✗
Add a model signature and re-log the model with registered_model_name.
Why it's wrong here
A model signature documents expected input and output schema, which helps validate inference data but does not control which model version a stage-based URI resolves to. Re-logging with the same registered name simply creates yet another version, and the batch job would still pick up whatever version currently sits in Staging. The signature changes nothing about version resolution.
- ✓
Load the model by explicit version URI instead of by stage.
Why this is correct
A version-based URI such as models:/<name>/3 resolves to one immutable artifact, so promoting a different version to Staging cannot alter what the batch job consumes. Promotion workflows in the Registry still function for other consumers. This decouples the batch job from stage transitions while preserving the governance and approval process the team relies on.
- ✗
Set the batch job to load the model from the run's artifact URI in the tracking server.
Why it's wrong here
The run artifact URI is immutable, but it bypasses the Model Registry entirely, discarding the promotion, approval, and lineage benefits the team wants to keep. It also requires the job to know a run ID, which is fragile and not a governance-friendly reference. This solves immutability at the cost of the Registry workflows the question says must remain intact.
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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-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.