Snowflake·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Snowflake coding screen, graph/topological sort territory. Pretty standard cycle detection problem but it's the kind of thing that trips you up if you haven't touched it recently.

Questions Asked (1)

Given a number of courses and a list of prerequisite pairs where each pair means one course must be completed before another, determine whether it's possible to finish all courses (i.e., detect if a cycle exists in the dependency graph).

Algorithms & Data Structures

Author's notes

Classic topological sort / cycle detection.

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AI HintsAI Generated

Suggested Approach

Model the courses and prerequisites as a directed graph, then detect cycles using either DFS with recursion stack or Kahn's topological sort algorithm. Explain the chosen method, walk through a small example, and analyze time and space complexity.

Pro tip: Mention that Kahn's algorithm can also produce a valid course order if one exists, and that early termination when the queue is empty but courses remain indicates a cycle. This shows you understand the algorithm's practical applications beyond just cycle detection.

1. Clarify and model the problem

Confirm that prerequisites form a directed graph where an edge from A to B means A must be taken before B. State that the problem reduces to detecting a cycle in this graph.

2. Choose an algorithm

Select either DFS with a recursion stack (colors: white, gray, black) or Kahn's topological sort (BFS with in-degrees). Briefly justify your choice based on simplicity or efficiency.

3. Walk through the algorithm

Explain step-by-step how the algorithm works on a small example, highlighting how cycles are detected (e.g., encountering a gray node in DFS or leftover nodes in Kahn's).

4. Analyze complexity

State that both approaches run in O(V + E) time and O(V + E) space, where V is the number of courses and E is the number of prerequisite pairs.

5. Discuss edge cases and extensions

Mention handling of empty input, disconnected graphs, and self-loops. Optionally, note that the same logic can return a valid course order if no cycle exists.

Key Points to Mention

Graph representation: adjacency list is efficient for sparse graphs.
Cycle detection via DFS: use three states (unvisited, visiting, visited) to detect back edges.
Kahn's algorithm: compute in-degrees, use a queue, and count processed nodes; if count < total, cycle exists.
Time and space complexity: O(V + E) for both methods.
Handling disconnected components: run algorithm from each unvisited node.
Practical application: course scheduling, build systems, task dependency resolution.

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