This is basically a variant of a knapsack problem but with two layers: you're picking flights AND packing cargo into them, and the flight cost only hits you if you actually book it.
Start by clarifying the problem constraints and objectives, then model it as an optimization problem. Propose a solution using dynamic programming or integer linear programming, and discuss trade-offs between optimality and efficiency.
Pro tip: Demonstrate awareness of real-world constraints like time windows and capacity, and suggest a greedy heuristic with proof of optimality under certain conditions, showing both theoretical and practical insight.
Ask about constraints: can cargo be split? Are flights one-time or recurring? What are the ranges of costs, capacities, deadlines? This ensures you address the correct problem.
Define decision variables: which flights to book (binary) and how much cargo to assign to each flight. Objective: maximize total revenue minus flight costs.
Recognize this as a variant of the knapsack problem with multiple knapsacks (flights) and time constraints (deadlines). Note that it may be NP-hard.
Suggest a dynamic programming approach if deadlines are small, or an integer linear programming formulation. For large instances, propose a greedy heuristic or approximation algorithm.
Discuss time complexity, optimality, and scalability. Mention potential improvements like column generation or Lagrangian relaxation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part tripped me up more than I expected.
Start by clarifying the streaming model: events arrive over time, and we need to maintain a current best plan that can be queried at any point. Then, describe how to adapt the batch solution to an incremental one, focusing on data structures that support efficient updates and queries, and discuss trade-offs between latency, throughput, and consistency.
Pro tip: Emphasize that the 'best plan' must be queryable at any time, so you need to maintain a valid state after each event; consider using a priority queue or balanced tree to keep the top candidate readily available, and discuss how to handle out-of-order events if they can occur.
Ask about event types (flights, cargo), arrival order (in-order or out-of-order), query frequency, and whether the plan must be exact or approximate. Confirm that the plan should be updated after each event.
Identify what constitutes the 'best plan' (e.g., max profit, min cost) and how a new event affects it. Determine if the plan can be updated incrementally or if recomputation is needed, and choose appropriate data structures (e.g., heaps, segment trees) to support efficient updates.
Outline components: an event ingestion layer, a state manager that applies updates, and a query interface. Discuss how to handle concurrency, backpressure, and fault tolerance if needed.
Compare time/space complexity of incremental updates versus batch recomputation. Discuss trade-offs between latency (immediate query response) and throughput (processing many events), and consider approximations if exactness is too costly.
Mention handling of out-of-order events, event time vs processing time, and potential need for windowing or late data. Suggest how to extend to distributed streaming (e.g., using Kafka, Flink) if scale increases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short follow-up but I actually liked this one.
Clarify the context first—whether this is a real-time booking system or a planning tool—then outline a robust failure-handling strategy using patterns like saga, compensation, and idempotency. Emphasize that the system should detect the failure, roll back or compensate, and maintain consistency while providing clear feedback to the user.
Pro tip: Show you think beyond just retries: discuss how you'd design for graceful degradation and eventual consistency, and mention monitoring/alerting to catch such failures early. This demonstrates a production-ready mindset that Optiver values.
Ask questions to understand the system's context: is it a real-time booking engine, a travel planning tool, or a distributed transaction? Determine consistency and availability requirements.
Map out where the booking attempt can fail (e.g., payment, inventory, network) and what downstream processes depend on the booking (e.g., itinerary, notifications). Assess the blast radius.
Propose patterns like saga for distributed transactions, compensation logic to undo partial work, and idempotent retries. Consider fallback options like alternative flights or manual intervention.
Explain how to maintain data consistency (e.g., eventual consistency, two-phase commit) and provide clear, timely feedback to the user with next steps.
Include logging, metrics, and alerts to detect failures. Suggest post-mortems and automated recovery to improve resilience over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.