This was the round I actually felt good about.
Start by clarifying the problem constraints (graph size, edge weights, directed/undirected, etc.) and then propose a baseline algorithm like BFS or DFS. After implementing the main solution, discuss potential optimizations and be prepared to extend it to handle variations such as weighted edges, multiple sources, or dynamic updates.
Pro tip: Always analyze time and space complexity before coding, and proactively suggest test cases including edge cases like disconnected graphs or cycles. This shows thoroughness and prevents bugs.
Ask questions to understand the graph representation, constraints, and expected output. Confirm whether the graph is directed/undirected, weighted/unweighted, and if there are any special conditions.
Select an appropriate traversal or pathfinding algorithm (e.g., BFS for unweighted shortest path, DFS for connectivity, Dijkstra for weighted graphs). Explain your choice and its complexity.
Write clean, modular code with clear variable names. Handle edge cases such as empty graphs, single node, or no path. Verbally walk through your code.
Run through test cases, including edge cases, to verify correctness. Discuss potential bugs and how you would fix them.
Address the follow-up extension by modifying your approach. Discuss trade-offs and possible optimizations (e.g., using a priority queue, bidirectional search, or A*).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints and edge cases first, then outline a step-by-step parsing strategy before coding. Choose an efficient algorithm (e.g., two-pointer, stack, or regex) and test with examples, handling edge cases like empty strings and special characters.
Pro tip: Communicate your thought process continuously and write clean, modular code; interviewers value clarity and correctness over speed. After coding, walk through a few test cases to demonstrate thoroughness.
Ask questions to clarify input format, output requirements, constraints, and edge cases. Confirm assumptions with the interviewer.
Outline a high-level algorithm, considering time and space complexity. Choose appropriate data structures and discuss trade-offs.
Write clean, well-structured code with meaningful variable names. Modularize logic for readability and handle edge cases explicitly.
Walk through examples, including edge cases, to verify correctness. If time permits, discuss potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.