← Early-stage Startup Interview Insights
Start by clarifying the problem scope (e.g., 9x9 grid, valid values 1-9, empty cells allowed) and then implement a straightforward validator using hash sets for rows, columns, and 3x3 sub-boxes. After confirming correctness, discuss and implement optimizations such as using bitmasks for constant-time checks and reducing space complexity.
Pro tip: Always discuss trade-offs between time and space, and mention that in an early-stage startup, simplicity and maintainability often trump micro-optimizations unless performance is a proven bottleneck.
Ask about grid size, allowed values, whether empty cells are permitted, and if the input is guaranteed to be well-formed. Confirm that validation means checking no duplicates in rows, columns, and 3x3 sub-boxes.
Write a clear, correct validator using hash sets or boolean arrays for each row, column, and sub-box. Iterate through the grid once, checking for duplicates and returning false if any are found.
State that the baseline runs in O(1) time (since the grid size is fixed at 81 cells) and O(1) space, but discuss how the constants matter and how the approach scales if the grid size were variable.
Replace hash sets with integer bitmasks to track seen digits for each row, column, and sub-box. Use bitwise operations to check and set bits, reducing overhead and improving cache performance.
Mention possible micro-optimizations like early exit, precomputing sub-box indices, or using a single pass with three arrays. Also discuss readability vs. performance and when each is appropriate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Second round, and yeah, it overlaps heavily with the first.
Start by clarifying requirements and constraints, then outline a modular design with separate components for validation, solving, and generation. Discuss algorithmic choices and trade-offs, emphasizing efficiency and scalability. Conclude with testing and potential optimizations.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle invalid inputs and performance under time pressure, and mention that generation can leverage the solver with randomized backtracking.
Ask about input format, expected output, performance requirements, and whether the solution should be optimized for speed or simplicity. Confirm if the generator should produce puzzles with unique solutions.
Explain how to validate a Sudoku board by checking rows, columns, and 3x3 subgrids for duplicates. Discuss using hash sets or bitmasks for O(1) checks per cell.
Describe a backtracking algorithm with constraint propagation, and mention optimizations like choosing the cell with fewest possibilities (MRV heuristic). Compare with exact cover / Dancing Links for harder puzzles.
Outline a two-step process: generate a complete valid board using randomized backtracking, then remove cells while ensuring a unique solution (using the solver to check uniqueness).
Compare time/space complexity of different approaches, and discuss testing strategies including unit tests for edge cases and performance benchmarks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.