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rippling·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Rippling SWE interview with a graph-based currency conversion problem. Pretty involved for a single question, they wanted a full algorithm walkthrough with complexity analysis and everything.

Questions Asked (1)

Q1

You have a set of directed currency exchange rates and a list of queries. Each rate lets you convert from one currency to another at a given multiplier, and you can chain multiple conversions together. For each query giving a source currency, target currency, and amount, find the maximum amount you can get after conversion, or return -1 if no path exists. Describe an efficient algorithm, the data structures you'd use, and give time and space complexity in terms of n rates and q queries.

Algorithms & Data StructuresSystem Design
Author's notes

This is basically a weighted directed graph problem where you want the max-product path between two nodes.

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AI HintsAI Generated

Suggested Approach

Model the currencies as nodes in a directed graph where edges are exchange rates, then for each query find the maximum product path from source to target. Use a modified shortest path algorithm (e.g., Bellman-Ford or Dijkstra with max-heap) that maximizes the product of rates, handling negative cycles and unreachable cases.

Pro tip: Mention that exchange rates can be converted to logarithms to turn multiplication into addition, allowing standard shortest path algorithms; but be careful with negative cycles and precision issues.

1. Model as a graph

Represent currencies as nodes and exchange rates as directed edges with weights equal to the rate. For each query, we need the maximum product path from source to target.

2. Choose algorithm

Use Bellman-Ford for maximum product paths to handle negative cycles (which correspond to arbitrage opportunities). Alternatively, use Dijkstra with a max-heap if no negative cycles exist, but Bellman-Ford is safer.

3. Preprocess if multiple queries

If many queries, consider precomputing all-pairs maximum products using Floyd-Warshall (with max and multiplication) or running Bellman-Ford from each source. For q queries, running per query may be acceptable if q is small.

4. Handle edge cases

Check for unreachable targets (return -1), and detect positive cycles (arbitrage) that could lead to infinite money. If a positive cycle is reachable and can reach the target, the maximum amount is unbounded (return -1 or special value).

5. Analyze complexity

For Bellman-Ford per query: O(n * m) time where n is number of currencies and m is number of rates, and O(n) space. For q queries: O(q * n * m). If using Floyd-Warshall: O(n^3) time and O(n^2) space.

Key Points to Mention

  • Graph representation: currencies as nodes, rates as directed edges with multiplicative weights.
  • Maximum product path problem: can be solved by negating logs and finding shortest path, or by modifying relaxation to use multiplication and max.
  • Bellman-Ford algorithm for handling negative cycles (arbitrage) and detecting unbounded paths.
  • Dijkstra with max-heap for efficiency if no negative cycles, but Bellman-Ford is more robust.
  • Time and space complexity: O(n*m) per query with Bellman-Ford, O(n^3) for all-pairs with Floyd-Warshall.
  • Edge cases: unreachable target (-1), positive cycles leading to infinite money (return -1 or special handling).

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