Databricks-ML-Pro ML Ops Practice Question
You are building a CI/CD pipeline that must promote an MLflow model version from Staging to Production in Databricks only after automated validation. The pipeline runs in a service principal context. Which two actions are required to implement this safely and repeatably? (Choose two.)
⚠ Common exam trap
The trap here is focusing on artifact storage or archiving policies, when the required elements are the service principal's model permission and the programmatic stage transition call.
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
✓
Grant the service principal CAN_MANAGE_PRODUCTION (or CAN_MANAGE) on the registered model so it can transition versions into Production.
Safe automated promotion needs two things: permission for the automation identity to change stages, and a programmatic call to perform the transition after validation. Granting the service principal CAN_MANAGE_PRODUCTION or CAN_MANAGE satisfies the first, and invoking the transition-stage API after validation satisfies the second. Archiving policies, artifact storage changes, and human tokens are neither required nor advisable for this pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Grant the service principal CAN_MANAGE_PRODUCTION (or CAN_MANAGE) on the registered model so it can transition versions into Production.
Why this is correct
Stage transitions in MLflow Model Registry are governed by model-level permissions. A service principal running the pipeline needs at least CAN_MANAGE_PRODUCTION or CAN_MANAGE to move a version into Production. Without this grant, the API call to transition the stage will fail with a permission error, making the pipeline non-repeatable. This is a required, concrete step for automated promotion.
- ✓
Configure the pipeline to call the MLflow Model Registry transition-stage API after validation succeeds, using the model name and version.
Why this is correct
Automated promotion requires a programmatic call. The transition-stage API (or its equivalent in the MLflow client) accepts the model name, version, and target stage, and performs the transition. Placing this call after the validation step ensures promotion only occurs on success. This is the core mechanism that makes the promotion repeatable and auditable, and it is required for the pipeline to function.
- ✗
Enable automatic model version archiving so that older Production versions are removed before the new version is promoted.
Why it's wrong here
Archiving older versions is a lifecycle policy choice, not a requirement for promotion. A new version can be transitioned to Production while previous versions remain in Production or are archived separately. Automatic archiving is not a feature that must be enabled for the transition to succeed, and enabling it could remove versions that auditors or rollback procedures still need. It is therefore not one of the required actions.
- ✗
Require the service principal to use a personal access token tied to a human user so that audit logs show a named approver.
Why it's wrong here
Service principals should authenticate with their own credentials or OAuth, not a human user's personal access token. Using a human token undermines the purpose of automation, creates a security risk if the user leaves, and produces misleading audit logs. It also does not grant any additional capability needed for the transition. This is an anti-pattern and not a required action for safe, repeatable promotion.
- ✗
Store the model artifacts in a Unity Catalog volume and reference the volume path when registering the model version.
Why it's wrong here
Model artifacts are stored in the run's artifact location and referenced by the model version automatically. Pointing registration at a Unity Catalog volume path is not necessary for stage transitions and does not affect whether the service principal can promote a version. While Unity Catalog can govern model access, it is not a required step to implement Staging-to-Production promotion in this pipeline, so it is not a correct choice.
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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.