Spent probably too long setting up the board structure before even touching the API surface.
Start by clarifying requirements and constraints, then outline the data structures (e.g., 2D array and column heights) and algorithms for drop, status check, and reset. Implement the core methods with clean code, and discuss optimizations and edge cases.
Pro tip: Mention that you would maintain a separate array of column heights to achieve O(1) drop and win-check, and discuss how to extend the design for a variable board size or AI opponent.
Ask about board dimensions, win conditions, input validation, and whether the engine needs to support undo or AI. Confirm expected time/space complexity.
Propose a 2D array (6x7) to represent the board and an array of size 7 to track the next available row per column. Discuss trade-offs with alternative representations.
Write drop(col, player) that validates the column, places the disc at the lowest empty cell, updates the column height, and checks for a win. Implement getStatus() to return win/draw/in-progress, and reset() to clear the board.
Explain how to check for a win efficiently by only examining lines through the last dropped disc (horizontal, vertical, two diagonals) rather than scanning the entire board.
Walk through edge cases (full column, full board, invalid input) and suggest possible extensions like variable board size, AI opponent, or persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Focus on the last placed disc and check only the four lines (horizontal, vertical, and two diagonals) passing through it. For each direction, count consecutive same-colored discs in both directions, and if the total reaches k, declare a win. This yields O(k) time because you examine at most 2k-1 discs per direction.
Pro tip: Mention that you can optimize further by precomputing directional offsets and using a single loop per direction, and note that this approach generalizes to any k-in-a-row game (e.g., Connect Four, Gomoku).
Start by locating the coordinates (row, col) of the most recently placed disc, as only lines through this disc can form a new winning sequence.
Consider the four axes: horizontal (0,1), vertical (1,0), diagonal (1,1), and anti-diagonal (1,-1). These cover all possible winning lines.
For each direction, move stepwise from the last move in both positive and negative directions, counting consecutive discs of the same color until a different color or boundary is hit.
If the total count (including the last disc) in any direction is at least k, return true; otherwise, continue to the next direction.
Explain that each direction requires at most 2k-1 checks, so total time is O(k) and space is O(1), as no additional data structures are needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty mechanical but they wanted explicit error types, not just a boolean.
Start by clarifying the context: is this a library API, a service endpoint, or an internal function? Then propose a consistent error-handling strategy that validates inputs early, returns structured errors, and avoids corrupting game state. Emphasize that invalid inputs should be rejected with clear, actionable feedback rather than silently ignored or causing crashes.
Pro tip: Treat invalid inputs as part of the API contract: define error types and document them, so clients can handle them programmatically. This shows you think about robustness and developer experience, not just correctness.
Ask whether this is a public API, internal function, or service, and what the caller expects (exceptions, error codes, Result types). Confirm if there are existing conventions in the codebase.
Check column bounds, board fullness, and player turn before any state mutation. Use guard clauses or a validation layer to centralize checks.
Decide between exceptions, error codes, or Result types based on language and API style. Ensure errors are descriptive and include context (e.g., 'Column 8 out of range 0-6').
Guarantee that invalid inputs do not partially mutate the game state. Validate before any changes, or use transactions/rollbacks if needed.
Add unit tests for each invalid input scenario and document the error behavior in the API reference. Consider logging for observability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I'd already parameterized the board dimensions but forgot to thread k through the win-check logic at first.
Start by clarifying the requirements: the board dimensions M and N, the connect length k, and the win condition (exactly k or at least k). Then outline a generalized algorithm that checks for k consecutive pieces in all directions, and discuss how to handle edge cases and optimize for performance.
Pro tip: Mention that you would first write a brute-force solution to validate correctness, then optimize by tracking only the last move's impact, since only that move can create a new winning line. This shows you understand both correctness and efficiency.
Ask about the exact win condition (exactly k or at least k), whether the board can be larger than memory, and if there are time/space constraints. Confirm that k can be any positive integer up to min(M, N).
For a given move at (r, c), check all four directions (horizontal, vertical, two diagonals) by counting consecutive same-colored pieces in both directions. If the total count reaches k, the player wins.
Instead of scanning the entire board after each move, only check around the last placed piece. This reduces time complexity from O(M*N) per move to O(k) per move, which is efficient for large boards.
Consider using a 2D array or a hash map for sparse boards. Handle cases where k=1 (immediate win) or k > min(M, N) (impossible to win). Also, ensure the algorithm works for any M, N, and k.
Compare time vs. space trade-offs: e.g., precomputing all possible winning lines vs. on-the-fly checking. Suggest unit tests for various board sizes and k values, including edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Bitboards came up and I had to be honest that I knew the concept but hadn't implemented one for Connect Four specifically.
Start by defining the two representations and their fundamental differences in memory layout and access patterns. Then compare them across key dimensions: time complexity for common operations (e.g., move generation, collision detection), space complexity, and cache performance. Finally, discuss practical trade-offs in the context of the game or application, mentioning when each is preferable.
Pro tip: Demonstrate awareness of real-world constraints: bitboards excel in performance-critical, memory-rich environments (e.g., chess engines), while 2D arrays offer simplicity and flexibility for dynamic or sparse boards. Mentioning specific examples like chess or Go shows depth.
Briefly explain what a 2D array and a bitboard are, including their memory layout (e.g., row-major vs. bit-packed) and typical use cases.
Analyze operations like accessing a cell, checking adjacency, generating moves, and detecting collisions. Highlight how bitboards use bitwise operations for O(1) parallel checks, while 2D arrays may require loops.
Discuss memory usage: 2D arrays store each cell explicitly (O(n^2) for n x n board), while bitboards pack bits (O(n^2 / word_size) words). Note overhead for multiple bitboards (e.g., one per piece type).
Mention cache locality, ease of implementation, flexibility for irregular boards, and language support for bitwise operations. These often outweigh theoretical complexity.
Summarize when to choose each: bitboards for performance-critical, dense boards with fixed size; 2D arrays for simplicity, dynamic boards, or when memory is constrained.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rattled off the obvious ones: full column rejection, win on last cell of board, draw state, alternating turns enforced.
Start by clarifying the game engine's core components and expected behaviors, then systematically outline unit tests for each module, emphasizing edge cases like boundary conditions, invalid inputs, and concurrency issues. Structure your answer around test categories (e.g., physics, rendering, input handling) and explain how each test isolates a unit and verifies correctness.
Pro tip: Demonstrate test-driven development (TDD) mindset by suggesting writing tests before implementation, and mention using mocks/stubs to isolate units and avoid flaky tests. Also, highlight the importance of testing deterministic behavior in game loops and handling floating-point precision edge cases.
Ask clarifying questions about the game engine's architecture, key modules (e.g., physics, rendering, input, audio), and expected behaviors to ensure you target the right units.
List the main units (classes/functions) and their responsibilities, then define the expected input-output behavior for each to guide test design.
For each unit, outline tests for typical scenarios and edge cases such as boundary values, null/empty inputs, overflow, and error conditions.
Discuss tests for interactions between units (e.g., collision detection triggering events) and non-functional aspects like performance and concurrency, if relevant.
Prioritize tests based on risk and impact, and explain why certain edge cases are critical (e.g., preventing crashes or ensuring fair gameplay).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.