← Early-stage Startup Interview Insights
Start by clarifying the problem and constraints, then define the state and recurrence relation before coding. Implement a bottom-up DP solution, optimize space if possible, and test with edge cases.
Pro tip: Always discuss time and space complexity trade-offs, and mention how you would optimize for the startup's scale. Showing awareness of practical constraints like memory usage can set you apart.
Ask questions to confirm input/output format, constraints, and edge cases. Ensure you understand what the DP is optimizing.
Clearly state what each DP cell represents (e.g., dp[i] = max value up to index i). This is crucial for a correct recurrence.
Express dp[i] in terms of previous states. Explain the transition logic and base cases.
Code the solution, starting with a straightforward bottom-up approach. Then consider space optimization (e.g., using rolling variables).
Walk through examples, including edge cases. State time and space complexity, and discuss potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.