The core idea isn't hard once you think about prefix sums and binary search, but I spent too long second-guessing the data structure.
Clarify requirements first (e.g., dynamic updates, thread safety, memory constraints), then propose a solution using prefix sums and binary search for O(log n) sampling, and discuss trade-offs with alternatives like the alias method or segment trees. Walk through the design, implementation details, and complexity analysis.
Pro tip: At Amazon, emphasize scalability and trade-offs: mention how your design handles large-scale data and concurrent access, and be ready to discuss when a simpler O(n) approach might be acceptable for small datasets.
Ask about expected operations (add, update, remove), frequency of updates vs. sampling, thread safety needs, and memory constraints to tailor the solution.
Suggest using a dynamic array of weights with prefix sums for O(log n) sampling via binary search, or a segment tree for O(log n) updates and sampling.
Describe how to generate a random number between 0 and total weight, then binary search the prefix sums to find the corresponding value.
State time and space complexity for each operation (add, sample) and compare with alternatives like the alias method (O(1) sampling but O(n) updates).
Mention handling updates, thread safety, and potential optimizations (e.g., Fenwick tree) and when to choose each approach based on use case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.