This part felt manageable but I fumbled a bit on stating assumptions out loud.
Start by restating each snippet in plain English, focusing on its purpose and behavior. Then systematically analyze time and space complexity for each, explaining your reasoning and considering best, average, and worst cases. Finally, compare the two snippets and discuss trade-offs.
Pro tip: Always clarify assumptions about input size and constraints before diving into complexity analysis, and mention that constant factors matter in practice even if Big-O ignores them.
Read each snippet carefully and restate its functionality in simple, non-technical terms. Identify inputs, outputs, and core operations.
Determine how the number of operations grows with input size. Consider loops, recursion, and built-in functions; specify best, average, and worst cases if they differ.
Assess additional memory used beyond the input, including variables, data structures, and call stack. Distinguish between auxiliary space and total space.
Highlight key differences between the two snippets in terms of efficiency, readability, and practical use. Mention scenarios where one might be preferred.
Provide a concise summary of your findings, reiterating the complexities and any important caveats or assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went straight to modeling a Piece class with four edges and a rotation state, then a Board class holding a 2D grid.
Start by clarifying requirements and constraints, then define the core domain model (Piece, Side, Board) with clear responsibilities and interfaces. Focus on the placement validation algorithm and rotation handling, and discuss trade-offs between simplicity and extensibility.
Pro tip: Emphasize the importance of separating the puzzle's logical model from its rendering, and propose an efficient validation strategy that checks only affected neighbors rather than the entire board.
Ask about puzzle size, piece representation, rotation mechanics, and whether the board is fixed or dynamic. Clarify if pieces can be placed anywhere or only in valid positions.
Identify key entities: Piece, Side, Board, and possibly Position. Define their attributes and relationships, such as Piece having four Sides with edge types, and Board managing a grid of placed pieces.
Specify methods like placePiece(piece, position, rotation), removePiece(position), and validatePlacement(piece, position, rotation). Discuss return types and error handling.
Detail how to check compatibility with adjacent pieces: compare side types (e.g., tab, blank, flat) after accounting for rotation. Consider edge cases like borders and empty neighbors.
Talk about trade-offs: in-memory vs. persistent storage, simple grid vs. sparse representation, and how to extend for different puzzle shapes or additional constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a clean data model (e.g., 4x4 grid, tile values as powers of 2, score). Walk through the move algorithm step-by-step, covering slide, merge, spawn, and game-over detection, while discussing time/space complexity and edge cases.
Pro tip: Emphasize that you separate the core game logic from the UI/rendering, making it testable and extensible; mention that you'd write unit tests for each move direction and merge scenario.
Ask about board size, win condition (2048 tile), spawn probability (90% 2, 10% 4), and whether moves that don't change the board are allowed. Confirm that the game is single-player and turn-based.
Represent the board as a 4x4 matrix (2D array) of integers, where 0 represents an empty cell and other values are powers of 2. Track the current score and game state (ongoing, won, lost).
For a given direction, extract each row/column in the direction of movement, slide non-zero tiles to one side, merge adjacent equal tiles (only once per tile per move), and update the score. Then place the resulting tiles back into the board.
After a successful move (board changed), spawn a new tile (2 or 4) in a random empty cell. Check for game over: no empty cells and no adjacent equal tiles. Also check for win if a 2048 tile is created.
Analyze time complexity: O(n^2) per move for an n x n board, which is O(1) for fixed 4x4. Mention potential optimizations like bitboards or precomputed move tables for larger boards, and how to make the code testable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.