← Hudson River Trading Interview Insights
The repeated-letter handling is where I stumbled.
Start by clarifying requirements and edge cases, then outline a constraint-based filtering approach using a candidate word list. Discuss how to handle repeated letters correctly by tracking minimum and maximum counts per letter, and describe the guessing loop with a max attempt limit. Finally, analyze time/space complexity and potential optimizations.
Pro tip: Demonstrate awareness of repeated-letter pitfalls by explicitly walking through an example like guessing 'e' when the answer has two 'e's but feedback shows one correct and one absent. This shows attention to detail and robustness.
Ask about word list size, allowed guesses, feedback format, and whether the solver must be optimal or just functional. Confirm if the word list is fixed or dynamic.
Define data structures to track exact positions, present letters with min/max counts, and absent letters. Explain how to update these from each guess's feedback.
For each candidate word, check if it satisfies all constraints: exact matches, letter presence with correct counts, and absence of forbidden letters. Handle repeated letters by counting occurrences.
Iterate: pick a guess (e.g., first candidate or heuristic), get feedback, update constraints, filter candidates. Stop when word found or max attempts reached.
Discuss time complexity (O(N * L) per guess) and possible optimizations like pre-indexing or entropy-based guess selection. Mention trade-offs between simplicity and optimality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a set of remaining candidates plus a constraint object tracking known positions, excluded positions per letter, and minimum/maximum counts per letter.
Start by clarifying the game rules and feedback format (e.g., Wordle-style with exact/partial matches). Then propose a data structure that tracks per-position constraints and global letter counts, and explain how to prune the candidate list using these constraints, emphasizing efficiency with bitsets or precomputed indices.
Pro tip: Mention that you can precompute a mapping from letters to words containing them at specific positions to enable O(1) lookups during pruning, and discuss trade-offs between memory and speed.
Ask about the exact feedback mechanism (e.g., green/yellow/gray) and whether constraints are per-position or global. Confirm the goal: maintain a set of possible words and update it after each guess.
Propose a structure with: (a) an array of sets for allowed letters per position, (b) a set of required letters with minimum counts, and (c) a set of forbidden letters. Optionally, track exact positions for green letters.
For each letter in the guess, update the constraints: green fixes a position, yellow adds to required letters and excludes from that position, gray excludes from all positions unless already required.
Use the constraints to filter the candidate list. For speed, represent each constraint as a bitset over the dictionary and intersect bitsets. Alternatively, use precomputed indices (e.g., letter-position to word list) to quickly narrow down.
Mention incremental pruning (only re-filter the current candidate list) and data structures like tries or inverted indices. Discuss time/space trade-offs and potential for parallelization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked through two options: picking the word that maximizes expected information gain (basically minimax entropy over the remaining candidates) versus a simpler frequency heuristic that scores words by how common their letters are in the remaining pool.
Start by clarifying the problem constraints (e.g., word length, dictionary size, allowed guesses) and then propose a hybrid strategy that balances letter-frequency heuristics with information-gain (entropy) to maximize expected reduction of the candidate set. Justify the trade-offs between computational cost and guess quality, and suggest a practical implementation that can adapt based on remaining possibilities.
Pro tip: Mention that in early guesses, maximizing information gain (e.g., using entropy over possible feedback patterns) is often more valuable than trying to guess the actual word, but as the candidate set shrinks, switching to a frequency-based heuristic can be more efficient. This shows you understand the exploration-exploitation trade-off.
Ask about the dictionary size, word length, allowed guesses, and whether feedback is exact (e.g., Wordle-style). This sets the scope and informs the choice of heuristic.
State that the goal is to minimize the expected number of guesses, which can be framed as maximizing information gain per guess or minimizing the expected size of the remaining candidate set.
Suggest using an information-gain (entropy) approach for early guesses to quickly narrow down possibilities, then switch to a letter-frequency or positional-frequency heuristic when the candidate set is small to pick the most likely word.
Discuss computational complexity: entropy calculation can be expensive (O(N^2) over candidates), but can be optimized with precomputation or sampling. Frequency heuristics are faster but less optimal. Choose based on time/memory constraints.
Describe how to implement: maintain a candidate list, compute feedback patterns for each possible guess, and select the guess that maximizes expected information gain or minimizes expected remaining candidates. Evaluate by simulating on a word list to compare average guesses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints and the feedback API's behavior, then design an algorithm that efficiently narrows down the dictionary using feedback. Implement the solve function with a loop that makes guesses, processes feedback, and tracks attempts, ensuring it handles edge cases like max_attempts exhaustion.
Pro tip: Discuss the trade-offs between different strategies (e.g., information gain vs. simplicity) and mention how you would test the solution with unit tests and mock feedback APIs. This shows you think about correctness and maintainability, not just getting a working answer.
Ask clarifying questions about the dictionary size, feedback format, and whether the feedback API is deterministic. Confirm the goal: return the final guess and number of attempts used, or indicate failure if max_attempts is exceeded.
Decide on an algorithm to select guesses, such as filtering the dictionary based on feedback (like in Wordle) or using a minimax approach to maximize information gain. Consider time and space complexity given potential large dictionaries.
Write code that initializes the candidate set, loops up to max_attempts, calls feedback_api with a guess, updates the candidate set based on feedback, and returns the final guess and attempt count. Handle cases where no candidates remain or attempts run out.
Create unit tests with mock feedback APIs to verify correctness, including edge cases like empty dictionary, immediate success, and failure after max_attempts. Discuss how to measure performance and optimize if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Each pruning pass is O(C * L) where C is the current candidate count and L is word length.
Start by clearly stating the time and space complexity of your solution using Big O notation, then walk through the reasoning by analyzing each part of your algorithm (e.g., loops, recursion, data structures). Finally, discuss any trade-offs and potential optimizations, especially in the context of large-scale systems like Pinterest.
Pro tip: Always relate the complexity to the problem constraints and Pinterest's scale—mention how your solution would perform with millions of users or petabytes of data, and if possible, suggest improvements for handling such scale.
Clearly and confidently state the time and space complexity of your solution in Big O notation, e.g., 'The time complexity is O(n log n) and space complexity is O(n).'
Break down your algorithm and explain how you derived the complexities, referencing specific parts like loops, recursive calls, or data structure operations.
Mention any trade-offs between time and space, and why you chose this approach over alternatives, considering factors like readability, simplicity, and performance.
Propose potential optimizations or alternative approaches that could improve complexity, and discuss their feasibility and impact.
Connect the complexity to Pinterest's scale, explaining how your solution would handle large inputs and whether further optimizations are needed for production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I wrote tests for the constraint update logic (especially the duplicate letter cases), the pruning function, and the full solve loop with a mocked feedback API.
First, briefly explain your testing strategy, covering edge cases and normal cases. Then, write clear unit tests for each component, and finally, walk through a sample run by tracing the execution with a specific input, showing how the tests pass and the solution works.
Pro tip: Use a table-driven test format to concisely cover multiple cases, and during the walkthrough, narrate your thought process to demonstrate systematic debugging and verification skills.
Explain what aspects of the solution you will test, including normal cases, edge cases, and potential failure modes. Mention the testing framework you'll use.
Write clear, isolated unit tests for each function or module, using descriptive names and assertions. Include tests for boundary conditions and invalid inputs.
Execute the tests and show the output, confirming all tests pass. If any fail, explain how you would debug and fix them.
Choose a representative input and manually trace the execution step by step, showing intermediate states and final output. Relate this to the passing tests.
Summarize how the tests validate the solution and discuss any trade-offs or limitations of your testing approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.