My first instinct was trie plus a heap, which is fine but I fumbled the deletion part pretty badly.
Start by clarifying requirements: define word boundaries, case sensitivity, and whether deletions remove words entirely. Then propose a trie augmented with frequency counts and a heap or sorted list at each node to retrieve top-k efficiently, and discuss trade-offs between update and query costs.
Pro tip: Mention that you would use a trie with a min-heap of size k at each node to cache top-k results, updating only along the path of the inserted/deleted word, which balances update and query efficiency.
Ask about word definition, case sensitivity, handling of deletions that reduce frequency to zero, and expected query patterns (e.g., prefix length, k size).
Propose a trie where each node stores a frequency count and a min-heap (or sorted list) of top-k words in its subtree, enabling O(prefix length + k log k) queries.
On insert, increment frequency and update heaps along the path; on delete, decrement and update similarly, removing words if frequency drops to zero.
Discuss time/space complexity: updates O(L log k) where L is word length, queries O(P + k log k) where P is prefix length; compare with alternative approaches like hash maps or suffix trees.
Mention optimizations like lazy updates, batch processing, or using a balanced BST for top-k, and consider concurrency and memory constraints for large-scale systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.