Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Snowflake SWE interview with a graph traversal problem that looks simple on the surface but has enough follow-up layers to trip you up if you're not careful about the details.

Questions Asked (1)

You have a start URI and a target URI, and an API that returns all URIs linked from a given page. Find the minimum number of clicks to get from start to target.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was DFS and I had to stop myself.

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AI HintsAI Generated

Suggested Approach

Model the problem as a graph where each URI is a node and directed edges represent links between pages. Use BFS to find the shortest path in terms of clicks, since BFS guarantees the minimum number of edges in an unweighted graph. Discuss optimizations like bidirectional BFS and handling large-scale graphs with distributed processing.

Pro tip: Mention that bidirectional BFS can significantly reduce the search space, especially when the branching factor is high, and discuss how to handle cycles and visited nodes to avoid infinite loops.

1. Clarify the problem

Confirm that the API returns all outgoing links from a page, and that we need the minimum number of clicks (edges) from start to target. Ask about constraints like graph size, memory limits, and whether the graph is directed.

2. Model as a graph

Represent each URI as a node and each link as a directed edge. The problem reduces to finding the shortest path in an unweighted directed graph.

3. Choose BFS for shortest path

Use Breadth-First Search (BFS) starting from the start URI, exploring level by level. BFS guarantees the minimum number of edges when the graph is unweighted.

4. Handle visited nodes and cycles

Maintain a visited set to avoid revisiting nodes and getting stuck in cycles. This also ensures O(V+E) time complexity.

5. Discuss optimizations and trade-offs

Mention bidirectional BFS to reduce search space, and consider distributed BFS for very large graphs. Discuss time vs. space trade-offs and API call overhead.

Key Points to Mention

BFS guarantees shortest path in unweighted graphs
Use a queue for BFS and a visited set to avoid cycles
Time complexity O(V+E) where V is number of URIs and E is number of links
Bidirectional BFS can reduce search space by exploring from both start and target
Handling large-scale graphs may require distributed processing or caching
API calls may be expensive; consider batching or parallel fetching

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