Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Snowflake software engineer interview, second round. One algorithmic problem involving arrays and distance calculations. Not much else to go on from what I remember.

Questions Asked (1)

You're given an array of 0s, 1s, and 2s. For each element that is a 1, find the distance to the nearest 2 in the array.

Algorithms & Data Structures

Author's notes

My first instinct was BFS and I think that's the right call, treat every 2 as a source and expand outward.

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AI HintsAI Generated

Suggested Approach

Clarify that the array contains only 0s, 1s, and 2s, and we need to compute for each 1 the distance to the nearest 2. Use a two-pass dynamic programming approach: first pass left-to-right to record distance to the nearest 2 on the left, second pass right-to-left to record distance to the nearest 2 on the right, then take the minimum for each 1. Alternatively, use a multi-source BFS from all 2s to compute distances to all cells, then extract distances for 1s.

Pro tip: Discuss edge cases upfront: no 2s in the array (return -1 or infinity), no 1s (return empty), and large arrays (O(n) time, O(n) space). Also, mention that the two-pass DP is optimal and simple, while BFS is more general if other values were present.

1. Clarify requirements and edge cases

Confirm that the array contains only 0, 1, 2; define distance as absolute index difference; handle cases with no 2s or no 1s.

2. Choose an approach

Select either two-pass DP (left-to-right and right-to-left) or multi-source BFS from all 2s. Explain why it's efficient (O(n) time).

3. Implement the algorithm

For two-pass DP: initialize distances to infinity, traverse left-to-right updating distance from last seen 2, then right-to-left updating distance from next seen 2, and take min. For BFS: enqueue all 2s, BFS to compute distances.

4. Extract and return results

Collect distances for indices where array value is 1. If no 2 exists, return -1 or infinity as per clarification.

5. Analyze complexity and test

State time and space complexity (O(n) time, O(n) space). Walk through a small example to verify correctness.

Key Points to Mention

Two-pass dynamic programming: left-to-right and right-to-left scans to compute nearest 2 distances.
Multi-source BFS from all 2s as an alternative, especially if the problem generalizes.
Time complexity O(n) and space complexity O(n) for the distance array.
Handling edge cases: no 2s present (return -1 or infinity), no 1s present (return empty list).
Distance defined as absolute difference in indices.
Optimization: can compute distances in-place if modifying the array is allowed, but typically we need to preserve original values.

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