Model Versioning for GenAI Quiz

What you'll be asked

1. What is the primary purpose of model versioning in a GenAIOps pipeline?
2. When deploying a fine-tuned LLM, why is it recommended to use a Model Registry instead of simple file-naming conventions like 'model_v1_final'?
3. In a GenAI context, which of the following should be considered a 'versioned artifact' alongside the model weights?
4. Which strategy best supports a 'Canary Deployment' for a new version of a Generative AI application?
5. When implementing immutable model versioning, what is the most significant risk of 'overwriting' a model artifact in storage (e.g., saving over 'model.bin' without a version tag)?

Answers and explanations come after you submit.