Your question is End-to-End Data Prep and Prediction. 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).
Describe how you would build an end-to-end pipeline that performs joins, filtering, null imputation, encoding, scaling, rounding, and then trains a model to predict price from transformed features.
Explain the pipeline stages, data contracts, train-serving consistency, orchestration, reproducibility, and validation strategy. Include how you would handle failed records, schema changes, reruns, feature leakage, and monitoring in a practical implementation.