The starting code was deliberately unprofitable, which was a bit disorienting at first because I spent a minute just reading it trying to figure out if I was missing something.
First, clarify the problem constraints and define the objective function. Then, model it as an optimization problem, likely reducible to min-cost flow or dynamic programming, and discuss algorithmic approaches with complexity analysis. Finally, address trade-offs and potential extensions.
Pro tip: Demonstrate awareness of real-world constraints like time windows and capacity, and mention how you would validate the solution with edge cases and stress tests.
Ask questions to understand the exact problem: Are flights and cargo independent? Can cargo be split? Are there multiple flights with same departure? What are the ranges of values? This ensures you solve the right problem.
Define variables: binary for flight selection, binary for cargo assignment to flights. Write objective: maximize sum(revenue of delivered cargo) - sum(cost of selected flights). Constraints: weight capacity per flight, cargo delivery deadline (cargo must be on a flight departing before latest arrival), and each cargo at most once.
Recognize this as a variant of the knapsack or facility location problem. Consider modeling as min-cost max-flow: source to cargo (capacity 1, cost -revenue), cargo to compatible flights (capacity 1, cost 0), flights to sink (capacity max weight, cost flight cost). Then find min-cost flow. Alternatively, use DP if constraints are small.
Discuss time and space complexity of the chosen approach. Compare with alternatives like greedy (not optimal) or integer programming. Mention that min-cost flow with potentials can be efficient for moderate sizes, but may be overkill for small inputs.
Consider cases like no flights, no cargo, all cargo too heavy, deadlines impossible. Discuss how to extend to multiple legs, time-dependent costs, or stochastic arrivals.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a system design lite question tucked into a coding interview.
Start by clarifying the user's goal and the system's constraints, then propose a layered solution that separates user-facing messaging from backend inventory logic. Emphasize transparency, graceful degradation, and real-time data synchronization to handle competitive purchases.
Pro tip: Frame the answer around user trust and system reliability: acknowledge that competitors may buy inventory, but focus on how your design minimizes surprise and provides actionable alternatives. This shows you think beyond code to business impact.
Ask clarifying questions about the system: Is this a real-time booking API? What data sources are involved? What are the latency and consistency requirements? This ensures you address the right problem.
Craft a clear, empathetic message that explains the flight is no longer available, without blaming the competitor. Offer alternative flights or waitlist options to maintain user engagement.
Use a combination of caching, real-time database queries, and possibly a reservation lock to verify availability. Handle race conditions with optimistic locking or distributed transactions.
Add logging, metrics, and alerts for inventory mismatches. Design fallbacks (e.g., retry with exponential backoff) and consider eventual consistency trade-offs.
Propose A/B testing different messages and monitoring user behavior. Use data to refine the balance between accuracy and user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.