Sierra AI·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Sierra AI SWE interview with a graph traversal problem centered on spreadsheet cell dependencies. Pretty focused session, just the one problem but they pushed on edge cases hard.

Questions Asked (1)

Write a function that traverses cell dependencies in a spreadsheet and detects circular references.

Algorithms & Data StructuresSystem Design

Author's notes

Classic cycle detection but the spreadsheet framing threw me a little at first.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Model the spreadsheet as a directed graph where cells are nodes and dependencies are edges. Use depth-first search (DFS) with a recursion stack to detect cycles, or topological sorting to identify circular references. Clearly explain the algorithm, handle edge cases, and discuss complexity.

Pro tip: Mention that in production systems, you'd also need to handle dynamic updates and incremental cycle detection, and that the solution should be efficient for large spreadsheets. This shows you think beyond the basic algorithm.

1. Clarify requirements and assumptions

Ask about the spreadsheet size, whether dependencies are static or dynamic, and if the function should return the cycle path or just a boolean. Confirm input format (e.g., adjacency list or cell formulas).

2. Model as a graph problem

Represent each cell as a node and each dependency as a directed edge. Explain that detecting circular references is equivalent to finding a cycle in a directed graph.

3. Choose an algorithm

Select DFS with a recursion stack (or colors: white, gray, black) for cycle detection, or use Kahn's algorithm for topological sorting. Discuss trade-offs: DFS is simpler and can return the cycle path; topological sort can detect cycles and provide evaluation order.

4. Implement and handle edge cases

Write pseudocode or code, ensuring to handle self-references, multiple cycles, and disconnected components. Consider iterative DFS to avoid stack overflow for large graphs.

5. Analyze complexity and discuss optimizations

State time complexity O(V+E) and space O(V). Mention potential optimizations like memoization for repeated subgraphs or incremental cycle detection for dynamic updates.

Key Points to Mention

Graph representation: adjacency list is efficient for sparse dependencies.
DFS with recursion stack: mark nodes as visiting/visited to detect back edges.
Topological sorting: if a topological order exists, no cycles; otherwise, cycle detected.
Handling self-references: a cell depending on itself is a cycle.
Complexity: O(V+E) time and O(V) space for DFS.
Edge cases: empty spreadsheet, multiple disconnected components, large graphs requiring iterative DFS.

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