Fine, wrote the loop, checked modulo, done in two minutes.
Start by clarifying the problem: confirm input format, output expectations, and edge cases. Then propose a simple linear scan solution, discussing time and space complexity. If appropriate, mention alternative approaches like list comprehensions or streams, but prioritize clarity and efficiency.
Pro tip: Demonstrate awareness of production concerns: ask if the list can be large and if memory is a constraint, then suggest a generator or streaming approach to handle large datasets efficiently.
Ask about input size, data types, output format (e.g., print each on new line or as a list), and whether the list can be empty or contain negative numbers.
Propose iterating through the list and checking each number's parity. Explain that you'll print odd numbers as you encounter them.
State that the solution is O(n) time and O(1) extra space (excluding output), which is optimal for this problem.
Mention other ways to implement, such as list comprehensions or filter functions, and note trade-offs (e.g., memory usage if creating a new list).
Address empty list, all even numbers, and negative odd numbers (e.g., -3 is odd). Confirm that the solution handles these correctly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said yes without thinking and the interviewer just waited.
Acknowledge that the modulo operator's behavior with negative numbers varies by language, so the solution must be explicit. Then explain how you would test and ensure correctness, such as using Math.abs or checking remainder != 0, and discuss trade-offs like performance and readability.
Pro tip: Mention that in languages like Python, -3 % 2 yields 1, while in Java it yields -1, so relying on n % 2 == 1 is a common bug. Show you're aware of these pitfalls and how to write portable code.
State that an integer is odd if it is not divisible by 2, i.e., n % 2 != 0. This definition holds for negative numbers.
Explain that the modulo operator can return negative results in some languages (e.g., Java, C++), so checking n % 2 == 1 fails for negative odd numbers.
Suggest using n % 2 != 0 or Math.abs(n % 2) == 1, and note that bitwise AND (n & 1) works for two's complement integers but may not be portable.
Compare readability, performance, and portability of different approaches, and mention that the choice depends on language and context.
Emphasize testing with negative numbers, zero, and extreme values to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: we need to perform arithmetic on numbers represented as strings, likely for operations like addition or multiplication. Then, outline a digit-by-digit algorithm that simulates manual arithmetic, handling carries and signs, and discuss trade-offs between different approaches. Finally, mention edge cases and potential optimizations.
Pro tip: Emphasize that you would first check if a built-in arbitrary-precision library (like BigInteger in Java or Python's int) is available, but be prepared to implement it manually if the interview requires it. This shows practical awareness and depth.
Ask about the specific operations needed (addition, subtraction, multiplication, division), input format (strings, possibly with signs), and performance expectations. This ensures you address the right problem.
For addition/subtraction, use digit-by-digit processing from least significant digit, managing carries/borrows. For multiplication, consider grade-school algorithm or Karatsuba for large numbers. Discuss trade-offs.
Account for negative numbers, leading zeros, empty strings, and invalid input. Normalize inputs by stripping leading zeros and determining sign.
Write clean code with helper functions for digit conversion and carry handling. Test with cases like '0', '999+1', large numbers, and negative results.
State time and space complexity (O(n) for addition, O(n*m) for multiplication). Mention optimizations like chunking digits or using FFT for very large numbers if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.