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Illumio·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jul 2026

Summary

Interviewed for a software engineering role at Illumio and got hit with the classic number of islands problem. Pretty standard coding round, nothing too surprising if you've done any graph traversal prep.

Questions Asked (1)

Q1

Given a 2D binary grid where '1' represents land and '0' represents water, count the number of distinct islands. Islands are formed by horizontally or vertically connected land cells.

Algorithms & Data Structures
Author's notes

I went with DFS, marking cells as visited as I sank them.

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AI HintsAI Generated

Suggested Approach

Treat the grid as a graph and use DFS or BFS to explore each unvisited land cell, marking all connected land cells as visited to count one island. Iterate through every cell, and when you find an unvisited '1', increment the island count and launch a traversal to mark the entire island.

Pro tip: Mention that you can optimize space by mutating the input grid (e.g., changing '1' to '0') if allowed, but clarify that in production code you'd avoid side effects or use a separate visited set. Also, discuss handling edge cases like empty grid or large grids that could cause stack overflow with recursive DFS.

1. Clarify and Validate

Confirm grid dimensions, connectivity (4-directional), and whether modifying the input is acceptable. Ask about edge cases like empty grid or all water.

2. Choose Traversal Strategy

Decide between DFS (recursive or iterative) and BFS. Discuss trade-offs: DFS is simpler but may overflow stack; BFS uses queue and is safer for large grids.

3. Implement Island Counting

Loop through each cell. When encountering an unvisited '1', increment count and perform traversal to mark all connected land cells as visited (e.g., set to '0' or use visited set).

4. Analyze Complexity

State time complexity O(M*N) since each cell is visited once, and space complexity O(M*N) in worst case for recursion stack or queue.

5. Test and Optimize

Walk through a small example, test edge cases, and mention potential optimizations like union-find or parallel processing for very large grids.

Key Points to Mention

  • Graph traversal algorithms: DFS and BFS
  • Visited tracking: modifying grid in-place vs. separate visited set
  • Time and space complexity analysis
  • Handling edge cases: empty grid, single cell, all land/water
  • Recursion depth limitations and iterative alternatives
  • Alternative approaches like Union-Find (Disjoint Set Union)

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