How would you measure a sales forecast model's accuracy?

This tests your ability to connect abstract metrics to concrete business costs. A strong answer first establishes the need for a test set, then defines MAE (interpretable, linear error cost) and RMSE (penalizes large errors quadratically). The choice depends on whether large misses are disproportionately more damaging to the business (e.g., stockouts). A red flag is giving only mathematical definitions or claiming one metric is universally superior, ignoring the crucial business context.