I spent probably too long defining what a 'word' even is before writing a single line of code.
Start by explicitly defining a tokenization policy that specifies how to handle contractions, hyphenated compounds, numerals, Unicode, and emojis, then implement a streaming word counter using a state machine or regex-based tokenizer that processes input in chunks. Discuss time and space complexity, and provide test cases covering edge cases and large files.
Pro tip: Emphasize that the tokenization policy is a product decision—clarify requirements with the interviewer before coding, and mention that streaming avoids loading the entire file into memory, which is crucial for scalability.
Ask clarifying questions about what constitutes a 'word' (e.g., are contractions one word or two? Should emojis count as words?). Explicitly state your policy for each edge case: contractions (e.g., 'don't' as one token), hyphenated compounds (e.g., 'well-known' as one token), numerals (e.g., '123' as a word), Unicode (e.g., accented characters as part of words), and emojis (e.g., each emoji as a separate token).
Outline a streaming approach that reads the file in chunks (e.g., 4KB blocks) and uses a finite state machine or regex to identify word boundaries. Handle partial tokens at chunk boundaries by carrying over state. For Unicode and emojis, consider using a library or code point iteration to correctly identify characters.
Write pseudocode or actual code for the streaming counter. Use a hash map to count word frequencies if needed, or just a counter for total words. Ensure the tokenizer correctly applies the policy from step 1. Discuss how to handle very large files without loading them entirely into memory.
State that time complexity is O(n) where n is the number of characters, as each character is processed once. Space complexity is O(1) for total word count (or O(k) for word frequency map where k is unique words), plus O(chunk size) for buffering. Emphasize that streaming keeps memory usage constant regardless of file size.
List test cases: empty file, file with only punctuation, contractions, hyphenated words, numerals, Unicode text, emojis, mixed content, and a large file to test streaming. Discuss trade-offs between different tokenization policies (e.g., simplicity vs. correctness) and potential performance implications of regex vs. manual state machine.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The cycle prevention piece is what made this interesting.
Start by clarifying the requirements and defining the Node class with parent and children references to enforce tree invariants. Implement each operation with careful pointer updates, ensuring cycle prevention by checking ancestry before moves. Analyze time and space complexity for each operation, highlighting trade-offs between simplicity and efficiency.
Pro tip: Mention that storing a parent pointer enables O(1) cycle detection during moveSubtree, but requires careful updates to maintain consistency. Also, discuss how the choice of data structure for children (e.g., list vs. set) affects removeChild complexity.
Confirm that the tree is rooted, nodes can have any number of children, and operations must maintain acyclic structure. Decide on storing parent and children references.
Write addChild, removeChild, and moveSubtree with proper pointer updates. For moveSubtree, check that the new parent is not a descendant of the moved node to prevent cycles.
Implement preorder (DFS) and breadth-first (BFS) traversals using recursion/stack and queue respectively. For find, traverse from root or use parent pointers to build path.
For each operation, state time and space complexity. Discuss how using parent pointers affects moveSubtree and find, and trade-offs between different child data structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic word break problem but the 'optimize for many queries on a fixed dictionary' angle changed things.
Start by clarifying the problem constraints and then propose a solution that preprocesses the dictionary into a trie for efficient word lookups. Use dynamic programming with memoization to determine if the string can be segmented, and backtrack to reconstruct a valid segmentation. Optimize for repeated queries by caching results and using the trie to quickly prune invalid paths.
Pro tip: Emphasize the trade-offs between preprocessing time and query time, and mention that for very long strings, an iterative DP with a trie avoids recursion depth issues and is more cache-friendly.
Ask about the expected length of strings, number of queries, dictionary size, and whether the dictionary is fixed. Confirm if any segmentation is acceptable or if there are preferences (e.g., longest words first).
Propose building a trie from the dictionary for O(L) word lookups, where L is the max word length. Consider using a set for O(1) lookups if the dictionary is small, but highlight trie's advantage for prefix pruning.
Define dp[i] as whether the substring from i to end can be segmented. Iterate from the end, and for each i, check all possible words starting at i using the trie. Store the next index for reconstruction.
Cache results for previously seen suffixes or entire strings. If the dictionary is fixed, precompute the trie once. Use memoization to avoid recomputing overlapping subproblems across queries.
After DP, if segmentation is possible, backtrack using the stored next indices to build the list of words. If not, return an empty result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The single transaction part took about two minutes.
Start by solving the single-transaction case with a linear scan tracking minimum price and max profit, then generalize to two transactions with cooldown and fee using dynamic programming with states (holding, sold, cooldown). Clearly define state transitions and validate with edge cases like decreasing prices or fees exceeding profits.
Pro tip: Emphasize the state machine formulation for the extended problem—it shows you can model complex constraints cleanly and reason about correctness, which Meta values for production code. Also, mention that the fee can be incorporated by subtracting it at the sell transition.
Confirm input format (array of prices), output (indices for single transaction, max profit for extended), and handle edge cases like fewer than 2 days, decreasing prices, or fees larger than any profit.
Iterate through prices, tracking the minimum price seen so far and the maximum profit; record buy and sell indices when a new max profit is found.
Define states: holding (bought but not sold), sold (just sold, must cooldown), and cooldown (ready to buy). Use DP arrays or variables to compute max profit after each day, incorporating the fee at sell and enforcing a one-day cooldown.
Argue that the DP considers all valid sequences of transactions respecting cooldown and fee, and that the single-transaction solution is a special case. State time O(n) and space O(1) for both.
Compare DP with alternative approaches like divide-and-conquer for two transactions without cooldown, and explain why DP is preferable with cooldown and fee. Mention that the solution can be adapted to return the actual transaction days if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.