Start by clarifying the problem requirements and constraints, then outline your approach before coding. For the backtracking problem, identify the decision space and pruning conditions early, and for the other problem, choose the optimal data structure and algorithm based on constraints. Manage time by allocating roughly 20 minutes per problem, leaving 5 minutes for review and edge cases.
Pro tip: Communicate your thought process continuously and write clean, modular code with meaningful variable names; this demonstrates engineering maturity and makes it easier to debug under time pressure.
Restate the problem in your own words, ask clarifying questions about input size, edge cases, and expected output format. Confirm assumptions before proceeding.
Discuss possible solutions, analyze time and space complexity, and select the most efficient one that fits the constraints. For backtracking, define the recursive function, base case, and pruning strategy.
Write code in small, testable chunks, explaining each part as you go. Use helper functions to keep the main logic clean and avoid bugs.
Walk through your code with a simple example, then test edge cases like empty input, single element, or large values. Fix any issues and optimize if needed.
If time permits, review for off-by-one errors, redundant computations, and potential improvements. Discuss trade-offs and alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.