Your question is Model and Dataset Versioning. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle versioning for both models and datasets?
Explain a practical design for making ML training and deployment reproducible. Cover how you identify immutable dataset snapshots, track transformations and feature definitions, associate each model with its exact training inputs and code, promote artifacts across environments, and roll back safely. Discuss how your approach would detect training-serving skew, support audits, and manage storage and retention. State which assumptions or scale details you would clarify before selecting specific tools.
