Model Versioning Strategies Quiz

What you'll be asked

1. What is the primary purpose of using a Model Registry in a machine learning lifecycle?
2. Which of the following best describes the 'Semantic Versioning' approach applied to ML models?
3. When promoting a model from 'Staging' to 'Production' in a registry, what is the most important technical requirement?
4. Why is it considered a best practice to store model metadata (e.g., training parameters, dataset version, framework version) alongside the model artifact?
5. In a scenario where you are managing multiple model versions, which strategy best handles 'Model Lineage' in a complex pipeline?

Answers and explanations come after you submit.