← Databricks Interview Insights
I got the basic win-check logic down pretty fast using row/col/diagonal counters per player, which is the standard trick.
Maintain row, column, and diagonal counters for each player to track progress toward a win, updating them in O(1) per move. On each move, validate the coordinates and cell emptiness, then increment the relevant counters and check if any reaches n. This achieves O(1) time and O(n) space.
Pro tip: Clarify upfront that the board state is stored separately (O(n^2) space) and that the O(n) space refers only to the auxiliary counters; this shows you understand the distinction and avoids confusion.
Confirm the board size n, player representation (e.g., 1 and 2), and that moves are 0-indexed. Discuss the space complexity: O(n) auxiliary space for counters, while the board itself takes O(n^2) if stored.
Use arrays of size n for row and column counts per player, and two scalars for the main diagonal and anti-diagonal counts per player. Also maintain the board (e.g., 2D array) to check cell occupancy.
Check if row/col are within bounds and the cell is empty; if not, return invalid. Otherwise, place the player's mark, increment the corresponding row, column, and diagonal counters (if applicable).
After updating counters, check if any of the incremented counters equals n. If so, return that the player has won; otherwise, return no winner yet.
Explain that each move is O(1) time and the auxiliary space is O(n). Discuss edge cases like n=1, invalid moves, and moves after a win.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the existing API contract and state model, then design reset() as a straightforward state reinitialization and undo() as a command-pattern or history-stack-based operation. Discuss trade-offs around memory, performance, and API semantics, and outline how you would test and document both methods.
Pro tip: Treat undo() as a reversible command with a bounded history to avoid unbounded memory growth, and explicitly define edge cases like undoing before any move or after a reset. This shows you think about production concerns, not just happy paths.
Ask about the current API shape, state representation, and whether undo should be single-level or multi-level. Confirm constraints like thread safety, persistence, and backward compatibility.
Define reset() to clear the board, reset turn and game status, and optionally clear undo history. Discuss whether it should return the new state or void, and how it interacts with in-flight games.
Propose storing moves as commands or snapshots in a stack, with a configurable max depth. Explain how to reverse a move (e.g., pop and restore previous state) and handle undo after reset or when history is empty.
Compare command pattern vs. full state snapshots in terms of memory and complexity. Cover concurrency, idempotency, error signaling (exceptions vs. result types), and how undo affects game-over states.
Describe unit tests for reset/undo sequences, boundary conditions, and concurrency. Mention updating API docs and versioning if the change is breaking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the part I was least prepared for.
Start by clarifying the engine's API and rules (board size, win conditions, move validation), then structure tests around the game lifecycle: initialization, valid moves, invalid moves, win detection, draw detection, and edge cases. Use a table-driven approach to cover many scenarios efficiently and assert both state changes and error handling.
Pro tip: Mention that you'd test the engine as a black box through its public API, and that you'd use property-based testing to generate random move sequences to uncover unexpected edge cases.
Ask about the engine's interface, board representation, win conditions, and error handling. Confirm assumptions before writing tests.
List categories: initialization, valid moves, invalid moves (repeated cell, out-of-bounds), win detection (rows, columns, diagonals), draw detection, and game state after game over.
For each category, enumerate specific scenarios including edge cases like early win, full board draw, and invalid inputs. Use equivalence partitioning and boundary value analysis.
Write tests that assert both the expected outcome (e.g., exception, error code) and the resulting board state. Use parameterized tests to reduce duplication.
Ensure all branches are covered. Consider property-based tests to generate random valid/invalid sequences and verify invariants (e.g., no two moves on same cell).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.