They didn't want code, just the reasoning, which honestly made it harder in a weird way.
Start by clarifying the problem and defining the state space as a graph, then explain how DFS systematically explores each branch to its full depth before backtracking. Walk through the recursive structure, covering base cases, visited tracking, and how you would handle cycles or large inputs.
Pro tip: At Netflix, interviewers value clean, production-ready thinking—mention how you'd avoid recursion depth issues (e.g., iterative DFS with an explicit stack) and how you'd test edge cases like disconnected graphs or deep paths.
Restate the problem to confirm understanding, then define what the nodes and edges represent. Identify whether it's a tree, DAG, or general graph, and note any constraints like cycles or directed edges.
Specify what information each recursive call needs (e.g., current node, visited set, path). Clearly state the base cases: when to stop recursion (e.g., null node, target found, leaf node).
Describe the recursive step: mark the current node as visited, process it, then recursively visit each unvisited neighbor. Explain how backtracking works and when to aggregate results.
Discuss handling of disconnected components, cycles, and deep recursion (stack overflow). Mention time and space complexity: O(V+E) time, O(V) space for visited set and recursion stack.
Briefly mention how to convert to iterative DFS using an explicit stack, and any optimizations like early termination or memoization if applicable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.