← Snowflake Interview Insights
Pretty approachable once you just think of it as a two-pointer or brute-force scan.
First, clarify the problem constraints and edge cases. Then, propose an efficient algorithm: scan the array once, tracking the last seen person and cake positions, and update the minimum distance whenever both are found. Finally, analyze time and space complexity and discuss potential optimizations.
Pro tip: Mention that the problem can be solved in O(n) time with O(1) space by tracking the most recent person and cake indices, and emphasize that this is optimal since you must examine each element at least once.
Ask if the array can be empty, if there can be multiple people and cakes, and confirm that distance is absolute difference in indices. Also, confirm return -1 if either type is missing.
Mention that a brute force would compare all pairs (O(n^2)), but an optimal single-pass approach tracks the last seen person and cake indices to achieve O(n) time.
Initialize last_person and last_cake to -1, min_dist to infinity. Iterate through the array; when encountering a person or cake, update the respective last index. If both have been seen, compute the absolute difference and update min_dist.
After the loop, if min_dist is still infinity, return -1; otherwise return min_dist. Also, consider if the array has only one type, return -1.
State that time complexity is O(n) and space is O(1). Discuss that this is optimal because any algorithm must read all elements in the worst case.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
BFS is the obvious move and I got there, but the part that tripped me up was the getLinks simulator.
Model the wiki as a graph and use BFS to find the shortest path in terms of clicks, since each edge has unit weight. Clearly define the getLinks function signature and discuss how to handle cycles and large graphs efficiently.
Pro tip: Mention that bidirectional BFS can significantly reduce search space when the branching factor is high, and always clarify assumptions about graph size and link retrieval latency.
Ask about graph size, whether links are directed, if getLinks can be called multiple times, and any latency or memory constraints.
Specify its input (page identifier) and output (list of linked pages), and discuss how to simulate or mock it for testing.
Explain why BFS guarantees the minimum number of clicks in an unweighted graph, and outline the BFS algorithm with a queue and visited set.
Address cycles, disconnected graphs, and potential optimizations like bidirectional BFS or caching getLinks results.
State time and space complexity (O(V+E) for BFS) and discuss trade-offs between different approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.