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Databricks·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026Remote

Summary

Databricks full-stack round that was basically a BFS deep dive. One problem, lots of follow-ups, and they really wanted to see if you'd handle every weird edge case without being prompted.

Questions Asked (1)

Q1

Given a matrix representing a graph with cells in different states (open, blocked, special), implement BFS to answer reachability or shortest-path queries. Handle edge cases like empty matrix, single-cell input, all cells blocked, start equals target, and multiple disconnected components. Also discuss time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I got the core BFS down pretty fast but then they just kept adding cases.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then outline a BFS solution using a queue and visited set, explaining how to handle special cells. Finally, analyze time and space complexity and discuss trade-offs.

Pro tip: Mention that BFS is optimal for unweighted graphs and discuss how to adapt if the graph is weighted or if multiple queries are needed (e.g., precompute distances).

1. Clarify the problem and edge cases

Ask clarifying questions about cell states, movement rules, and query types. Explicitly list edge cases like empty matrix, single cell, all blocked, start equals target, and disconnected components.

2. Outline BFS approach

Describe using a queue for BFS, a visited set to avoid cycles, and handling special cells (e.g., treat as open or apply specific rules). Explain how to check reachability or compute shortest path.

3. Handle edge cases in code

Explain how each edge case is handled: return early for empty matrix or start equals target, check if start/target are blocked, and ensure BFS covers all components if needed.

4. Analyze complexity

State time complexity O(R*C) for BFS on a grid, and space complexity O(R*C) for the queue and visited set. Discuss if multiple queries change complexity.

5. Discuss trade-offs and optimizations

Mention alternatives like bidirectional BFS for single-pair queries, precomputing distances for multiple queries, or using DFS for reachability (but not shortest path).

Key Points to Mention

  • BFS guarantees shortest path in unweighted graphs.
  • Use a queue and a visited set (or mark visited in-place) to avoid revisiting cells.
  • Edge cases: empty matrix, single cell, all blocked, start equals target, disconnected components.
  • Time complexity O(R*C) and space complexity O(R*C) for BFS on a grid.
  • Special cells: clarify if they are passable or have special rules; handle accordingly.
  • Trade-offs: bidirectional BFS for single-pair queries, precomputation for multiple queries.

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