Capital One·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026Remote

Summary

Capital One technical screen focused entirely on matrix manipulation. One problem, five operations, and a lot more edge cases than I expected going in.

Questions Asked (1)

Given an m x n integer matrix, implement five operations: swap two rows, swap two columns, reverse a given row, reverse a given column, and rotate the entire matrix 90 degrees clockwise. Keep in mind the matrix may not be square, so rotation must produce an n x m result.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The first four operations were fine, pretty mechanical.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the matrix representation (e.g., list of lists) and whether operations should mutate in place or return new matrices. For each operation, discuss the straightforward approach and its time/space complexity, then optimize where possible (e.g., in-place swaps, rotation without extra space). Emphasize handling non-square matrices correctly, especially for rotation.

Pro tip: For rotation, you can avoid extra space by performing a transpose followed by reversing each row, but be careful with non-square matrices—transpose changes dimensions. Alternatively, create a new n x m matrix and map indices directly. Always test with a non-square example to catch dimension errors.

1. Clarify requirements and constraints

Ask about matrix representation, whether operations should mutate in place or return new matrices, and if there are any constraints on time/space complexity. Confirm that rotation must produce an n x m matrix.

2. Design each operation

For swap rows/columns, describe direct index swapping. For reverse row/column, describe in-place reversal using two pointers. For rotation, explain the index mapping: new[i][j] = old[m-1-j][i] for clockwise rotation.

3. Analyze time and space complexity

State that each operation is O(m*n) in the worst case (e.g., rotation) or O(n) for row operations and O(m) for column operations. Discuss space: in-place for swaps/reversals, O(m*n) for rotation if creating a new matrix.

4. Discuss trade-offs and optimizations

Compare in-place vs. new matrix for rotation: in-place is possible but requires careful index manipulation and may be less readable. Mention that for non-square matrices, in-place rotation is not straightforward because dimensions change.

5. Test with examples

Walk through a small non-square matrix (e.g., 2x3) for each operation, especially rotation, to verify correctness and catch off-by-one errors.

Key Points to Mention

Matrix representation (e.g., list of lists) and whether operations mutate in place or return new matrices.
Time complexity: O(n) for row operations, O(m) for column operations, O(m*n) for rotation.
Space complexity: in-place for swaps/reversals, O(m*n) for rotation if creating new matrix.
Rotation index mapping: new[i][j] = old[m-1-j][i] for clockwise rotation.
Handling non-square matrices: rotation changes dimensions from m x n to n x m.
Edge cases: empty matrix, single row/column, and large matrices.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.