Assess students' ability to design an end-to-end algorithmic dynamic pricing and inventory-control strategy for a multinational e‑commerce retailer that maximizes expected profit under stochastic demand. The quiz should test skills in (1) formulating a stochastic decision model (state, actions, constraints, objective) that incorporates price elasticity, competitor pricing responses, periodic promotions, multi-market inventory and lead times; (2) building and integrating machine‑learning demand and elasticity forecasts with uncertainty quantification; (3) selecting and justifying a solution approach (stochastic dynamic programming, model predictive control, approximate dynamic programming) and providing algorithm/pseudocode; and (4) proposing evaluation methods (simulation/backtesting, KPI metrics, sensitivity and robustness analysis) and practical implementation considerations (computation, scalability, risk limits, data requirements). Limit scope to undergraduate advanced-level methods and provide clear assumptions and deliverables.