← Microsoft Interview Insights

Microsoft·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Jun 2026

Summary

Microsoft SWE interview with a grid-based coding problem that was a follow-up to something I'd seen before, which sounds great until you realize the variation trips you up anyway.

Questions Asked (1)

Q1

Given a 2D grid where cells with a value of 4 or less are considered 'cloud' cells, find the area of the largest connected cloud region using 4-directional connectivity.

Algorithms & Data Structures
Author's notes

I'd done the count-clouds version before so I figured this would be easy.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Treat the grid as a graph where each cloud cell (value ≤ 4) is a node, and edges connect 4-directionally adjacent cloud cells. Use DFS or BFS to explore each connected component, tracking the size of each, and return the maximum size found. This is a classic connected components problem on a grid.

Pro tip: Clarify edge cases upfront (empty grid, no cloud cells, all cloud cells) and mention that you can mutate the grid to mark visited cells (e.g., set to -1) to save space, but note the trade-off of modifying input. Also, discuss iterative vs recursive DFS to avoid stack overflow on large grids.

1. Clarify and Validate

Confirm the definition of 'cloud' (value ≤ 4), connectivity (4-directional), and edge cases (empty grid, no cloud cells). Ask about grid size constraints to choose the right algorithm.

2. Choose Traversal Method

Decide between DFS (recursive or iterative) and BFS. Both are O(m*n) time; BFS uses a queue and avoids recursion depth issues, while DFS is simpler to code.

3. Implement Traversal

Iterate through each cell; when a cloud cell is found, start a traversal to explore the entire connected component, counting cells. Mark visited cells to avoid revisiting (e.g., set to -1 or use a visited set).

4. Track Maximum Area

After each traversal, compare the component size to the current maximum and update if larger. Continue until all cells are processed.

5. Analyze Complexity and Optimize

State time complexity O(m*n) and space complexity O(m*n) in worst case (e.g., all cloud cells). Discuss potential optimizations like early termination if max area exceeds remaining cells.

Key Points to Mention

  • Grid as implicit graph: cells are nodes, adjacency is 4-directional.
  • DFS/BFS for connected components; iterative BFS avoids recursion stack overflow.
  • Visited marking: mutate grid (e.g., set to -1) or use separate visited matrix; discuss trade-offs.
  • Time complexity O(m*n) and space complexity O(m*n) worst case.
  • Edge cases: empty grid, no cloud cells, all cloud cells, single row/column.
  • Potential follow-up: 8-directional connectivity or dynamic updates.

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