This one took me a while to even frame properly.
Start by clarifying requirements and scale, then propose an inverted index over targeting attributes to quickly retrieve eligible campaigns, combined with a priority queue or budget-aware ranking. Discuss how to handle frequent updates using incremental index updates and lazy deletion, and analyze time/space trade-offs for matching, insertion, and deletion.
Pro tip: Emphasize that real ad systems often use a hybrid approach: a coarse inverted index for filtering and a fine-grained scoring model for ranking, and mention that budget constraints can be enforced via a separate real-time bidding component.
Ask about the number of campaigns, request rate, update frequency, and latency requirements to determine if an in-memory or distributed solution is needed.
Propose an inverted index mapping each targeting attribute (e.g., location, device) to a set of campaign IDs, and a separate structure for budget tracking.
For an incoming request, intersect the posting lists of its attributes to get eligible campaigns, then rank by bid and budget using a priority queue or sorted list.
Use incremental updates to the inverted index for additions/removals, and consider lazy deletion or tombstones to avoid costly rebalancing.
Discuss time complexity for matching (e.g., O(k * log n) for k attributes), insertion/deletion (O(1) amortized), and space overhead; compare with alternatives like bitmap indexes or tree-based structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
TSP question dressed up in delivery clothing.
Start by framing the problem as a Vehicle Routing Problem (VRP) and clarify that exact solutions are NP-hard, so heuristics are needed for large N. Then walk through exact methods (e.g., DP for small N) and heuristics (e.g., nearest neighbor, savings, 2-opt, metaheuristics), analyzing time/space complexity. Finally, discuss how to extend the model to handle capacity and time windows, emphasizing trade-offs and practical implementation.
Pro tip: Demonstrate awareness that real-world routing often uses hybrid approaches (e.g., clustering then routing) and that Amazon's scale demands distributed, incremental optimization with live traffic data.
Restate the problem as a VRP, confirm assumptions (e.g., symmetric graph, metric), and ask about scale (N) and real-time requirements. This shows you understand the problem space before diving into algorithms.
For small N, mention exact methods like Held-Karp DP (O(N^2 2^N)) or branch-and-bound, and note they become infeasible as N grows. Analyze time and space complexity clearly.
Describe constructive heuristics (nearest neighbor, Clarke-Wright savings) and improvement heuristics (2-opt, 3-opt, Lin-Kernighan). For larger instances, mention metaheuristics (simulated annealing, genetic algorithms) and their typical complexity (e.g., O(N^2) per iteration).
Explain how to incorporate capacity (CVRP) and time windows (VRPTW) by modifying the objective and constraints, and using specialized heuristics (e.g., savings with capacity, insertion heuristics with time windows). Discuss trade-offs between optimality and feasibility.
Outline a practical implementation: preprocess graph (e.g., compute shortest paths), choose algorithm based on N, and consider distributed or incremental optimization for large-scale systems. Mention evaluation metrics (total distance, runtime).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.