AAIA AI Operations Practice Question
A company uses MLflow for model registry and tracking. You need to transition a model from the 'Staging' environment to 'Production' only after it passes an automated integration test. How is this state transition managed in MLflow?
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
✓
Using the 'transition_model_version_stage' API
Transitioning model versions in the MLflow Model Registry is done via the 'transition_model_version_stage' API or UI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Using the 'transition_model_version_stage' API
Why this is correct
This API call allows for programmatic state management of registry versions.
- ✗
Updating the 'version' tag in the git repo
Why it's wrong here
Git tags track code, not model registry stages.
- ✗
Deleting the model in Staging and re-uploading to Production
Why it's wrong here
This loses the history and version association.
- ✗
Manually copying files in the S3 bucket
Why it's wrong here
This bypasses the tracking system and breaks lineage.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official ISACA exam blueprint
This AAIA practice question is part of Courseiva's free ISACA 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 AAIA exam.