The logic itself isn't hard once you see it, each item's weight maps to exactly one rate bracket and you multiply and sum.
Clarify the input format and pricing table structure, then design an algorithm that iterates through each item, determines its weight tier, and accumulates the cost. Discuss handling edge cases like weights exactly on tier boundaries and rounding, and analyze time complexity.
Pro tip: Mention that you would sort the pricing table by weight ranges and use binary search for O(log n) tier lookup per item, demonstrating optimization for large orders. Also, explicitly state that you would use integer arithmetic for currency to avoid floating-point errors, then round only at the end.
Ask about input format (e.g., list of weights, pricing table as list of (min, max, rate)), whether ranges are inclusive/exclusive, and rounding rules. Confirm that each item is priced independently based on its weight.
Outline a function that takes the weights and pricing table, and for each weight, finds the applicable rate and adds weight * rate to the total. Discuss naive linear search vs. optimized binary search after sorting the table.
Address weights exactly on boundaries, weights outside the table (e.g., error or default), and rounding to two decimal places. Recommend using integer cents or Decimal to avoid floating-point issues.
State time complexity (O(n log m) with binary search, where n is number of items and m is number of tiers) and space complexity. Walk through a small example to verify correctness.
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