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The no-float constraint is the whole point of the problem and it took me a minute to internalize why.
Clarify the rounding rule (e.g., round half up) and edge cases like negative numbers and trailing zeros. Then design a string-based algorithm that identifies the integer part, fractional part, and applies rounding by comparing the first fractional digit and handling carry propagation. Finally, discuss trade-offs such as time complexity and potential precision issues.
Pro tip: Explicitly state your rounding rule and handle negative numbers correctly, as many candidates overlook this. Also, mention that you avoid floating-point parsing to prevent precision loss, which is crucial for ML applications.
Ask the interviewer about the rounding rule (e.g., round half up, half even) and how to handle negative numbers, leading/trailing zeros, and very large numbers. Confirm the expected output format.
Split the input string at the decimal point. If no decimal point, return the integer part as is. Handle optional sign and leading zeros.
Look at the first digit of the fractional part. If it's 5 or more (for round half up), increment the integer part; otherwise, keep it. For negative numbers, apply the rule to the absolute value and then reapply the sign.
If rounding up, add 1 to the integer part string, handling carries (e.g., '999' + 1 = '1000'). Ensure the result has no leading zeros unless it's zero.
Analyze time and space complexity (O(n) time, O(n) space). Mention alternative rounding rules (e.g., banker's rounding) and how to extend the solution. Highlight that avoiding float parsing prevents precision errors.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem constraints (e.g., string formats, precision, rounding rule) and then outline a solution that parses the strings into a suitable numeric representation (e.g., Decimal or integer scaling) to avoid floating-point errors. Implement the rounding by dividing the number by the precision, rounding to the nearest integer, and multiplying back, then format the result as a string. Discuss trade-offs between using built-in decimal libraries versus manual integer arithmetic, and consider edge cases like negative numbers and very large values.
Pro tip: Emphasize the importance of avoiding floating-point inaccuracies by using integer arithmetic or decimal libraries, and proactively mention how you would test the function with edge cases like negative numbers and trailing zeros.
Ask about the expected input formats, rounding rule (e.g., round half up, half even), handling of negative numbers, and whether the precision can be negative or zero.
Decide whether to use a decimal library (e.g., Python's decimal.Decimal) or scale to integers to avoid floating-point errors, considering performance and precision needs.
Compute the quotient of the number divided by the precision, round it to the nearest integer using the specified rule, then multiply by the precision to get the rounded value.
Convert the rounded value back to a string, ensuring correct formatting (e.g., preserving trailing zeros if required) and handling edge cases like negative zero.
Walk through test cases including positive/negative numbers, different precisions, and boundary conditions to verify correctness and discuss potential pitfalls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.