Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Snowflake SWE interview with a grid-based assignment problem that builds on a classic cake-and-person setup. The core challenge was getting the greedy heap approach right and handling the follow-up constraint about determinism cleanly.

Questions Asked (1)

You have a 2D grid with people and cakes placed on it. Each person will eat the cake closest to them, and each cake can only be consumed by one person. Given a specific person's index, return which cake that person ends up with. You're guaranteed no two people share the same distance to any cake, and no two cakes share the same distance to any person.

Algorithms & Data Structures

Author's notes

The constraints are what make this tractable.

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AI HintsAI Generated

Suggested Approach

Model the problem as a bipartite matching between people and cakes, where edges represent the closest cake for each person. Since each person greedily takes their closest available cake, simulate the process by sorting all person-cake pairs by distance and assigning cakes in order, skipping already taken cakes. For the specific person, track when their closest available cake is assigned.

Pro tip: Clarify whether the process is simultaneous or sequential; if simultaneous, ties are broken by the guarantee, but if sequential, the order of people matters. Also, consider using a priority queue or sorting to efficiently find the closest available cake for each person.

1. Understand the problem

Restate the problem: each person wants the closest cake, but cakes are limited to one person each. The goal is to determine which cake a given person gets, assuming a stable matching where each person takes their closest available cake.

2. Model as bipartite matching

Create a bipartite graph with people on one side and cakes on the other. For each person, compute distances to all cakes and sort them. The matching should assign each person to their closest cake that is not taken by someone with a higher priority (closer distance).

3. Simulate the greedy assignment

Sort all person-cake pairs by distance. Iterate through pairs in increasing distance; if both person and cake are unassigned, match them. Continue until all people are matched or no more pairs. This yields the unique stable matching due to the distance uniqueness guarantee.

4. Extract the specific person's cake

After simulation, return the cake assigned to the given person's index. If the person is not matched (should not happen if enough cakes), handle edge cases.

5. Analyze complexity and optimize

The naive approach is O(P*C log(P*C)) due to sorting all pairs. Optimize by using a priority queue per person or spatial indexing (e.g., k-d tree) to find closest cakes efficiently, but for interview, discuss trade-offs.

Key Points to Mention

Bipartite matching and stable matching concepts
Greedy algorithm with sorting by distance
Time and space complexity analysis (e.g., O(P*C log(P*C)) vs. optimized approaches)
Handling uniqueness guarantees to avoid ties
Edge cases: insufficient cakes, person index out of bounds, multiple people with same closest cake
Potential use of priority queues or spatial data structures for efficiency

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