Recognized it as combination sum pretty fast, which helped.
Clarify the problem constraints (e.g., can items be reused, are there modifiers/sides, what's the expected output format) before diving into solutions. Then discuss a recursive backtracking approach to generate all combinations, and analyze time/space complexity while considering optimizations like sorting and pruning.
Pro tip: Mention that in a real DoorDash scenario, you'd likely need to handle large menus and multiple target sums efficiently, so you might precompute combinations or use dynamic programming if the target is small, but always confirm with the interviewer about the scale and whether approximate solutions are acceptable.
Ask about input size, whether items can be reused, if modifiers/sides are independent items, and the desired output format (e.g., list of item IDs or counts).
Propose a backtracking solution to explore all combinations, or dynamic programming if the target is small and items are reusable. Discuss trade-offs between generating all combinations vs. finding one.
Describe the recursive function: sort items, iterate, include/exclude current item, recurse with reduced target, and backtrack. Handle base cases (target == 0, target < 0, or index out of bounds).
State time complexity (exponential in worst case) and space complexity (recursion depth). Suggest optimizations like sorting to skip duplicates and pruning when the current sum exceeds the target.
Address how modifiers/sides affect the problem (treat as separate items or nested combinations), and how to handle large datasets (e.g., using meet-in-the-middle or approximation).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.