← Google Interview Insights

Google·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Google SWE coding round with a board simulation problem. Not the hardest thing I've seen but the edge cases tripped me up more than I'd like to admit.

Questions Asked (1)

Q1

Given a one-dimensional board with pieces and coins, find the maximum number of coins that can be collected by moving pieces left-to-right, where a piece collects a coin by landing on it and no two pieces can occupy the same cell.

Algorithms & Data Structures
Author's notes

My first instinct was greedy and I think that's mostly right, just match each piece to the nearest coin to its right without overlapping assignments.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Clarify the problem constraints (e.g., board size, number of pieces, coin positions) and define the goal precisely. Then propose a dynamic programming solution that processes cells from left to right, maintaining the maximum coins collectible given the last occupied cell. Discuss time and space complexity and possible optimizations.

Pro tip: Explicitly state your assumptions about the input format and constraints before diving into the solution, as this demonstrates thoroughness and prevents misunderstandings. Also, mention that you would test edge cases like no coins or pieces blocking each other.

1. Clarify the problem

Ask questions to understand the board representation, piece movement rules, and constraints (e.g., can pieces move any distance? Are coins only collected when landed on? Can pieces skip over coins?).

2. Define the state and recurrence

Define DP state as the maximum coins collectible up to cell i, considering the last piece placed. Derive a recurrence that either skips the current cell or places a piece to collect a coin if available.

3. Implement and optimize

Write pseudocode for the DP, then analyze time and space complexity. Consider optimizations like using a 1D array or greedy approach if applicable.

4. Test with examples

Walk through a small example to verify correctness, including edge cases such as no coins, multiple pieces, and pieces that cannot move.

Key Points to Mention

  • Dynamic programming approach with state representing the last occupied cell
  • Time complexity O(n^2) or O(n) with optimization, space complexity O(n)
  • Greedy approach may not work due to overlapping constraints
  • Handling of coins and pieces as separate entities
  • Edge cases: no coins, no pieces, pieces at boundaries
  • Potential to reduce to a maximum independent set or interval scheduling problem

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