In my first coding round, I was asked to solve a graph-based grid problem. The challenge revolved around applying BFS to compute the shortest path, which required designing an efficient traversal while carefully managing edge cases.

It wasn’t just about implementing the algorithm, but also about handling visited states correctly, avoiding redundant computations, and ensuring the solution scaled well for larger inputs.

From this experience, I realized that interviewers often use grid-based problems to test both algorithmic fundamentals and implementation accuracy. BFS, DFS, Dijkstra, and 0-1 BFS are essential techniques that frequently appear in such scenarios.

My key takeaway: practice shortest path problems very thoroughly. These not only strengthen your graph fundamentals but also improve your ability to reason about state, memory usage, and efficiency under pressure.

They asked me to work on a grid traversal scenario where I had to determine the minimum steps to reach a target cell, ensuring efficient use of BFS and careful edge case handling.