First, clarify the requirements and constraints, then outline a solution that handles exact, prefix, and anagram matches efficiently. Discuss preprocessing options like building a trie for prefixes and a hash map for anagrams, and compare with on-the-fly computation, highlighting trade-offs in time, space, and update complexity.
Pro tip: Demonstrate awareness of real-world constraints: if the book list is static, preprocessing yields faster queries; if dynamic, a hybrid approach or on-the-fly may be better. Also, mention that anagram matching can be optimized by sorting characters or using a character count signature.
Ask about data size, query frequency, update frequency, and whether matches are per word or whole title. Confirm case-insensitivity and exact match definition.
For each title, split into words. For each word, check exact match (lowercased equality), prefix match (startswith), and anagram match (sorted characters equality). Return titles with any matching word.
If queries are frequent, preprocess: build a trie for prefix matches, a hash map from sorted word to titles for anagrams, and a set for exact matches. This reduces query time to O(1) or O(prefix length).
Compare preprocessing vs. on-the-fly: preprocessing uses more memory and requires updates when data changes, but gives faster queries. On-the-fly is simpler and uses less memory but slower per query.
Based on assumptions (e.g., static data, high query volume), recommend a hybrid or preprocessing approach. Mention potential optimizations like caching or indexing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.