Databricks-ML-Pro ML Ops Practice Question
A team runs a nightly Databricks job that retrains a demand-forecasting model and registers a new version in the MLflow Model Registry. Compliance requires that the model version used for scoring in production be immutably identified and that any subsequent retraining not silently change what production serves. Which practice best satisfies this requirement?
⚠ Common exam trap
The trap here is believing that referencing the registered model name always serves a fixed artifact, when it actually resolves to whatever version the current stage or alias points to.
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
✓
Assign a stage or alias to the specific model version and have the scoring job reference the model by that stage or alias, updating it only through a controlled promotion step.
Pinning production to a specific model version through a stage or alias gives an auditable pointer that only changes via an explicit promotion. Referencing the model name alone, inventing new names per run, or reading raw artifact paths all allow production to drift or lose lineage, so they fail the immutability and traceability requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Register each retrained model under a new registered model name that encodes the training date, and update the scoring job to read the newest name each night.
Why it's wrong here
Creating a new registered model per run fragments lineage and forces the scoring job to guess the newest name, which is error-prone and not immutable because the job's logic changes what it reads over time. It also complicates governance, since version history and transitions are spread across many model names instead of one controlled lineage.
- ✗
Store the model artifact path from the training run in a Delta table and have the scoring job read the path directly, bypassing the Model Registry entirely.
Why it's wrong here
Bypassing the registry loses stage or alias-based promotion, lineage, and access controls that compliance relies on. A raw artifact path can be overwritten or garbage-collected, and there is no controlled audit of which path production uses, so immutability and traceability are weaker than a registry-pinned version.
- ✗
Have the scoring job reference the model by its registered model name only, so that the latest version is always used after each nightly run.
Why it's wrong here
Referencing only the model name resolves to whichever version is currently in the target stage or alias, so a nightly retraining that transitions a new version would silently change what production serves. This directly violates the immutability requirement, because the scored artifact is not pinned and can change without an explicit production promotion.
- ✓
Assign a stage or alias to the specific model version and have the scoring job reference the model by that stage or alias, updating it only through a controlled promotion step.
Why this is correct
Stages and aliases provide an indirection pointer to a specific model version. By promoting only through a controlled step, the scoring job always resolves to the exact version that was validated, and retraining that registers new versions does not alter production until an explicit promotion changes the pointer, satisfying immutability and traceability.
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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-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.