Already seen this one in the phone screen, so I finished it pretty fast and just asked to move on.
Start by clarifying the problem constraints and expected output, as the question is intentionally vague. Then outline a parsing strategy using string splitting and conversion, and discuss how to process the resulting numbers based on likely interpretations (e.g., sum, max, or validation).
Pro tip: Always ask clarifying questions before coding; it shows you think about edge cases and requirements, which is crucial for ambiguous problems at top companies like Uber.
Ask the interviewer to specify what 'process' means (e.g., sum, product, max, or validate format) and any constraints like input size or allowed characters.
Split the string by the delimiter '-', then convert each substring to an integer. Handle potential errors like empty tokens or non-numeric characters.
Based on clarified requirements, compute the desired result (e.g., sum all numbers, find max, or check if sequence is valid).
Walk through the given example '1-4-3-8' and edge cases (e.g., single number, empty string, invalid format) to verify correctness.
State time and space complexity: O(n) time for splitting and processing, O(n) space for storing tokens (or O(1) if processing on the fly).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the replacement problem after I flagged the first one as a repeat.
Use dynamic programming to compute the largest square of 1s ending at each cell, tracking the maximum side length. Then return the square of that maximum side length as the area.
Pro tip: Clarify whether the grid contains only 0s and 1s and whether the square must be axis-aligned; also discuss space optimization to O(n) to show depth.
Ask about grid dimensions, values (e.g., 0/1), and whether the square must be contiguous and axis-aligned. Confirm expected output is area, not side length.
Let dp[i][j] be the side length of the largest square ending at (i, j). If grid[i][j] == 1, dp[i][j] = 1 + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]); else 0.
Iterate through the grid, compute dp values, and keep track of the maximum side length seen. Handle base cases for first row and column.
Return maxSide * maxSide. State time complexity O(m*n) and space complexity O(m*n), then mention space can be optimized to O(n) using a 1D array.
Walk through a small example, e.g., [[1,0,1],[1,1,1],[1,1,1]] to verify. Discuss edge cases like empty grid, all 0s, or all 1s.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.