Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer registers a model to the Databricks Model Registry and moves it to the 'Production' stage. A downstream batch scoring job references the model as models:/churn_model/Production. A data scientist then registers a new model version and transitions it to 'Production'. What happens to the downstream batch scoring job the next time it runs?
⚠ Common exam trap
The trap here is assuming stage URIs are pinned when a job is defined, when they are actually resolved dynamically at each model load.
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 job loads the newly promoted Production version automatically, because the models:/churn_model/Production URI resolves to whichever version currently holds that stage.
Stage-based model URIs like models:/churn_model/Production resolve dynamically at load time to the version currently assigned to that stage. When a new version is transitioned into Production, subsequent scoring runs automatically use it, allowing controlled promotion without editing job code. The URI is not pinned or cached at job creation.
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 job fails because a model URI cannot reference a stage once more than one version has occupied that stage.
Why it's wrong here
The Model Registry is designed to allow many versions to move through stages over time. A stage URI remains valid and always resolves to the single version currently in that stage. Multiple historical versions in Production do not invalidate the URI, so the job does not fail for this reason.
- ✗
The job continues to load the previously promoted version, because model URIs are cached and pinned at the time the job was first created.
Why it's wrong here
Model stage URIs are not cached or pinned at job creation; they are resolved dynamically each time the model is loaded. The registry tracks which version occupies each stage, so a later stage transition changes what the URI resolves to. This distractor describes a behavior that does not exist in the Model Registry.
- ✓
The job loads the newly promoted Production version automatically, because the models:/churn_model/Production URI resolves to whichever version currently holds that stage.
Why this is correct
Stage-based URIs such as models:/churn_model/Production are resolved at load time to the model version currently assigned to that stage. After the new version is transitioned to Production, the next job run picks up that version, enabling controlled rollout without changing job code, which is exactly the intended behavior.
- ✗
The job loads the newest registered version regardless of stage, because the models:/ URI ignores stage names and always selects the highest version number.
Why it's wrong here
The models:/ URI format explicitly supports stage names and resolves to the version in that stage, not to the highest version number. A newly registered version that has not been transitioned to Production would not be loaded. Ignoring stage names would defeat the purpose of staged promotion and gated releases.
Visual reference
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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.