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Passed all test cases on this one so I can't really complain about my solution.
Start by clarifying the problem constraints and edge cases, then identify the graph type (directed/undirected, weighted/unweighted) and choose the appropriate algorithm (BFS, DFS, Dijkstra, topological sort, etc.). Implement the solution with clean code, analyze time and space complexity, and test with examples including edge cases.
Pro tip: Microsoft interviewers value clean, bug-free code and clear communication; before coding, briefly outline your plan and after coding, walk through a test case to demonstrate correctness.
Ask questions to understand input format, constraints, edge cases, and expected output. Confirm whether the graph is directed/undirected, weighted/unweighted, and if there are any special conditions.
Determine if it's a traversal, shortest path, connectivity, cycle detection, topological sort, or something else. This guides algorithm selection.
Select the optimal algorithm (e.g., BFS for unweighted shortest path, DFS for cycle detection) and appropriate data structures (adjacency list, queue, stack, priority queue, visited set).
Write clean, modular code with meaningful variable names. Test with provided examples and edge cases (empty graph, disconnected components, cycles).
State time and space complexity. If needed, discuss potential optimizations or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
DP problems are hit or miss for me but this one clicked.
Start by clarifying the problem constraints and edge cases, then define the state and recurrence relation before coding. Implement the DP solution iteratively, optimizing space if possible, and test with small examples to verify correctness.
Pro tip: In an online assessment, prioritize a correct and efficient solution over premature optimization; write clean code with meaningful variable names and handle edge cases explicitly to avoid hidden test failures.
Restate the problem in your own words, identify input/output, and ask clarifying questions about constraints, edge cases, and expected time/space complexity.
Determine what each state represents (e.g., dp[i] = optimal solution for first i elements) and how it relates to subproblems.
Derive the transition between states, considering all possible choices at each step, and establish base cases.
Code the DP solution, starting with a straightforward top-down or bottom-up approach, then optimize space or time if needed.
Run through small examples, edge cases (empty input, single element, large values), and verify against a brute-force solution if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.