Software Engineer · Phone Screen · Coding

Google — Software Engineer ✅ Passed

Level: Unknown Level

Round: Phone Screen · Type: Coding · Difficulty: 7/10 · Duration: 60 min · Interviewer: Unfriendly

Topics: Graphs, Dijkstra's Algorithm, Shortest Path, Data Structures, Algorithms

Location: Mountain View, CA

Interview date: 2025-12-31

Summary

Round 1: Coding

Question: I was asked to solve a graph problem similar to LeetCode 778, involving finding a path from start to finish in a grid where the cost is the maximum value encountered along the path. I solved it using a modified Dijkstra's algorithm.

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Details

The coding question I got was:

1. Main Problem (similar to LeetCode 778 - Swim in Rising Water)

Given a grid representing a state map, find the path from the start to the end where the path cost is not the sum of the paths, but the "maximum/worst value encountered on the path" (similar to water level rising; you can only move if the water level >= the height of the cell).

My approach was to directly use a variant of Dijkstra's algorithm:

• dist[x] represents the "minimum possible maximum" to reach this point.
• When expanding neighbors:

new = max(dist[cur], height[nxt])

Use a min-heap to run to the end. The complexity is approximately O(n^2 log n) (depending on the grid size). The interviewer was receptive to this.

I also mentioned that I could use binary search for the water level + BFS/DFS to determine connectivity, but Dijkstra's algorithm is cleaner.

2. Follow-up (similar to LeetCode 743 - Network Delay Time)

The scenario is a general graph: given edge weights (directed/undirected based on the problem), find the shortest time to transmit a signal from a source to all points, output the maximum shortest path (or return -1 if some points are unreachable).

This is a standard Dijkstra's algorithm:

• Build an adjacency list.
• Initialize dist to INF, source = 0.
• Pop the minimum dist from the heap and relax the edges.
• Finally, check if there is any INF in dist; if so, return -1; otherwise, return max(dist).

The interviewer asked why the cost is not a normal sum and why Dijkstra's algorithm can still be used (the key is that it "satisfies the optimal substructure + each time the minimum dist point is determined, it will not be updated to be smaller"). They also asked about corner cases, such as the start point = end point, unreachable nodes, and how to handle duplicate entries in the heap (using visited or if curDist!=dist[u] continue).

My approach:

1. I identified that Dijkstra's algorithm could be adapted to solve both problems by modifying the cost calculation and relaxation steps.
2. I explained my reasoning for using Dijkstra's, emphasizing the optimal substructure property and how it applies to these specific problems.
3. I addressed the corner cases raised by the interviewer, demonstrating my understanding of potential issues and how to handle them.

Key insights:

• Understanding the underlying principles of Dijkstra's algorithm allows for adaptation to various pathfinding problems.
• Careful consideration of edge cases and potential optimizations is crucial for a robust solution.
• Clearly communicating the reasoning behind algorithmic choices is important for demonstrating understanding to the interviewer.

LeetCode similar: LeetCode 778, LeetCode 743