← Meta Interview Insights

Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Meta SWE interview with a maze traversal problem. Pretty classic DFS setup but the constraint of only having two methods to work with made it more interesting than your typical graph question.

Questions Asked (1)

Q1

A mouse needs to find cheese in a maze. You're given only two methods: one to check if the current position has cheese, and one to move to an adjacent position. Design an algorithm using depth-first search to navigate the maze.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The constraint that you can't see the full maze upfront is what makes this tricky.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the maze as a graph where each position is a node and moves to adjacent positions are edges. Use recursive DFS with a visited set to explore all reachable positions, checking for cheese at each step and backtracking when necessary. Clearly state assumptions about the maze boundaries and movement rules.

Pro tip: Discuss how to handle cycles and avoid infinite loops by marking visited positions, and mention that DFS uses O(V) space in the worst case due to recursion depth. Also, clarify whether the maze is finite and if the cheese is guaranteed to be reachable.

1. Clarify the problem and assumptions

Ask about the maze representation, movement rules (e.g., 4-directional), and whether the maze is finite. Confirm that the cheese is reachable and that we can mark positions as visited.

2. Define the DFS function

Write a recursive function that takes the current position, checks if it has cheese (return true if found), marks it as visited, and then recursively explores all adjacent unvisited positions.

3. Handle backtracking and termination

If none of the adjacent moves lead to cheese, return false to backtrack. Ensure the recursion terminates when all reachable positions are visited or cheese is found.

4. Analyze complexity and trade-offs

State that time complexity is O(V + E) where V is number of positions and E is number of moves, and space is O(V) for the visited set and recursion stack. Mention that BFS could find the shortest path but DFS is simpler and uses less memory in some cases.

Key Points to Mention

  • Use a visited set to avoid infinite loops in cyclic mazes.
  • Recursive DFS naturally backtracks when a path is exhausted.
  • Check for cheese at the start of each DFS call to handle the starting position.
  • Time complexity is O(V + E) and space complexity is O(V) due to recursion stack and visited set.
  • DFS does not guarantee the shortest path; BFS would be better if shortest path is required.
  • Consider iterative DFS with an explicit stack to avoid recursion depth limits.

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