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The column tracking is the real puzzle here.
Use a BFS traversal while tracking each node's horizontal distance (column index) from the root. For each column, maintain the deepest node seen so far, updating when a node is deeper or at the same depth but further right. Finally, collect the characters from the leftmost to rightmost column and concatenate them.
Pro tip: Clarify the tie-breaking rule upfront: 'rightmost' means the node with the largest horizontal distance within the same column? Actually, within a column, rightmost is ambiguous; typically it means the node that appears later in a level-order traversal (i.e., further right in the tree). Confirm with the interviewer to avoid misinterpretation.
Ask clarifying questions about the tie-breaking rule for 'rightmost' within the same column and depth, and confirm the output format (e.g., string of characters).
Use BFS (level-order) to process nodes level by level, which naturally handles depth. Maintain a map from column index to the best node (deepest, then rightmost) found so far.
For each node, compute its column index (root at 0, left child -1, right child +1). If the column is new or the current node is deeper, or same depth but appears later in BFS order (rightmost), update the map.
After traversal, determine the min and max column indices, then iterate from min to max, appending the character from the best node in each column to form the result string.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify that the follow-up asks for the same result but with columns reversed (right-to-left). Then, adapt your previous solution by either reversing the column order in the output or iterating columns from last to first, ensuring the row order remains unchanged.
Pro tip: Mention that reversing column order can be done in-place by swapping symmetric columns, which is O(n) time and O(1) space, and discuss trade-offs with creating a new matrix.
Confirm that 'reverse column order' means the first column becomes the last, the second becomes the second-last, etc., while rows stay in the same order.
Briefly restate the approach used for the original problem (e.g., matrix traversal, transformation) to establish a baseline.
Modify the original solution by either reversing the column indices during output or by swapping columns symmetrically in-place.
State the time and space complexity of the adapted solution, noting any changes from the original.
Walk through a small example (e.g., 2x3 matrix) to verify the column reversal works correctly and edge cases are handled.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.