Google·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Google SWE coding round, one problem, felt like a deceptively simple board game puzzle that turned into a real headache once I started thinking about edge cases.

Questions Asked (1)

You have a 1D board (string of up to 100 characters) with empty cells, coins, and one or more tokens. Each move, pick any token and slide it exactly 3 cells to the right. Tokens can't share a cell. A token only collects a coin if it lands on that cell (not cells it jumps over). What's the maximum number of coins you can collect?

Algorithms & Data Structures

Author's notes

My first instinct was greedy, just move each token toward the nearest coin.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the problem as a state-space search where each state is the positions of all tokens and the set of collected coins. Use BFS/DFS with memoization to explore all possible move sequences, but optimize by noting that tokens are indistinguishable and moves only increase positions. Alternatively, since tokens never move left, consider dynamic programming over the board from left to right, tracking token positions modulo 3 and coin collection.

Pro tip: Clarify constraints early: ask about the maximum number of tokens and whether tokens can be moved multiple times. This shows you think about edge cases and scalability, which is crucial for a Google interview.

1. Understand the problem and constraints

Restate the problem in your own words, confirm the rules (move exactly 3 right, no sharing, coin only on landing), and ask clarifying questions about token count and board size.

2. Identify the state space and transitions

Define a state as the tuple of token positions (sorted) and the set of collected coins. A move consists of choosing a token, moving it +3 if the target is empty and within bounds, and updating collected coins if the target has a coin.

3. Choose an algorithm (BFS/DFS with memoization or DP)

Since moves only increase positions, the state graph is a DAG. Use DFS with memoization to compute the maximum coins from each state, or BFS to explore all reachable states and track the max coins.

4. Optimize using problem-specific insights

Note that tokens are indistinguishable, so sort positions. Also, tokens' positions modulo 3 are invariant, which can reduce state space. Consider DP over board index with token positions modulo 3.

5. Analyze complexity and test edge cases

Discuss time/space complexity (e.g., O(N * C * 2^C) for DP) and test with small boards, multiple tokens, and coins at unreachable positions.

Key Points to Mention

State representation: sorted token positions and collected coins bitmask.
Moves only increase positions, so the state graph is a DAG, enabling DP/memoization.
Tokens are indistinguishable, so sort positions to avoid duplicate states.
Invariant: each token's position modulo 3 never changes.
Coin collection only on landing, not jumping over.
Complexity analysis and potential optimizations for up to 100 cells.

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