My first instinct was hash set, which is correct, but I said it too fast and then fumbled when they asked me to justify the memory tradeoff versus a trie.
Start by clarifying requirements and constraints, then propose a hash-based solution (e.g., hash set) for O(1) average lookup, explaining why it's better than a trie for exact-match queries. Discuss normalization and case-folding upfront, and address duplicate queries with caching or deduplication.
Pro tip: Mention that you'd preprocess the dictionary once and reuse it across queries, and that for Unicode normalization, you'd use NFC or NFD consistently. Also, note that if memory is a concern, a Bloom filter could be a space-efficient alternative, but it has false positives.
Ask about memory limits, latency requirements, and whether the dictionary is static or dynamic. Confirm if queries are case-sensitive and what Unicode normalization form is expected.
For exact-match lookups, a hash set offers O(1) average time and is simpler than a trie. If prefix searches are needed, consider a trie, but here it's overkill.
Normalize both dictionary words and queries to a consistent Unicode form (e.g., NFC) and apply case folding (e.g., to lowercase) if case-insensitive. Be explicit about the chosen normalization and its implications.
Use a cache (e.g., hash map) to store results of previous queries, or deduplicate the query list before processing to avoid redundant lookups.
Compare hash set vs. trie vs. Bloom filter in terms of time, space, and accuracy. Mention that Bloom filters can reduce memory but introduce false positives, which may be unacceptable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.