Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Google SWE interview with a grid-based constraint satisfaction problem. The kind of question that looks like competitive programming but has enough depth to keep you second-guessing your approach for the whole hour.

Questions Asked (1)

Given an N by M grid and k crop types, each with a required count that sums to N*M, produce a valid grid layout where every crop appears exactly the prescribed number of times and all cells of the same crop form a single 4-connected region.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This one took me a few minutes to even understand what the constraints were actually demanding.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a constructive algorithm that partitions the grid into contiguous regions for each crop. Discuss the trade-offs between different partitioning strategies (e.g., row-major, spiral, recursive division) and analyze time/space complexity.

Pro tip: Mention that the problem is always solvable by a simple row-major fill if you order crops appropriately, but demonstrate deeper insight by discussing how to handle non-rectangular regions and optimize for cache efficiency.

1. Clarify Requirements and Constraints

Ask about grid dimensions, crop counts, and whether any layout is acceptable or if specific properties (e.g., compactness) are desired. Confirm that counts sum to N*M and that each count is at least 1.

2. Choose a Partitioning Strategy

Select a method to divide the grid into k contiguous regions with the required areas. Options include row-major allocation, recursive splitting, or spiral filling. Consider simplicity vs. shape quality.

3. Implement and Validate

Write code to assign crops to cells according to the strategy, ensuring each region is 4-connected. Validate by checking counts and connectivity (e.g., via BFS/DFS).

4. Analyze Complexity and Trade-offs

Discuss time and space complexity (typically O(N*M)) and trade-offs between strategies (e.g., row-major is simple but may create thin regions; recursive division yields more compact shapes but is complex).

5. Handle Edge Cases and Optimizations

Address cases like k=1, k=N*M, or counts that force awkward shapes. Mention potential optimizations like using a union-find to verify connectivity or precomputing region boundaries.

Key Points to Mention

The problem is a variant of grid partitioning with area constraints and connectivity requirements.
A simple row-major fill works if crops are ordered by their counts, but may produce non-compact regions.
Recursive division (e.g., splitting the grid into rectangles) can yield more balanced regions but requires careful handling of remainders.
Connectivity can be ensured by construction (e.g., filling row by row) or verified post-hoc with BFS/DFS.
Time complexity is O(N*M) for most constructive approaches; space complexity is O(N*M) for the grid.
Trade-offs include simplicity vs. region compactness, and potential parallelization for large grids.

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