The Number of Black Blocks problem gives you an m×n grid (initially all white) and a list of black cell coordinates. A "block" is a 2×2 subgrid. For each number k from 0 to 4, count how many 2×2 blocks contain exactly k black cells. This Number of Black Blocks coding problem uses hash map enumeration over cell contributions to 2×2 blocks.
Uber, Twitter, Visa, Stripe, Roblox, Block, and Capital One ask this to test the ability to enumerate which 2×2 blocks are affected by each black cell and aggregate counts efficiently — without iterating over all m×n possible blocks. The array, hash table, and enumeration interview pattern is directly applied.
Enumerate affected blocks per black cell. Each black cell (r, c) belongs to up to four 2×2 blocks: their top-left corners can be at (r-1,c-1), (r-1,c), (r,c-1), (r,c). For each valid corner (within grid bounds), increment blockCount[corner] in a hash map. After processing all black cells, the hash map values give the number of black cells in each 2×2 block. Count blocks by their value to fill result[0..4]. Blocks not in the hash map (value=0) are counted as (m-1)*(n-1) - len(blockCount).
Grid 4×4. Black cells: (0,0), (1,1), (0,1).
"How many k-cell sub-grids exist?" problems are solved by inverting the perspective: instead of scanning each sub-grid, for each colored cell, enumerate which sub-grids it belongs to (at most 4 for a 2×2 block). This flip in perspective reduces O(m*n) scanning to O(B) where B is the number of black cells. Practice this "enumerate contributions" pattern for 2D problems — it appears in "number of rectangles," "count sub-matrices," and "block coloring" problems across interviews at Stripe, Uber, and Block.
