Passed all the test cases so I guess it went fine.
Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures and algorithms. Walk through your thought process, analyze time and space complexity, and finally implement clean, modular code with test cases.
Pro tip: Verbalize your reasoning and trade-offs even if you don't finish coding—interviewers at IBM value clear problem-solving and communication over silent perfection. Also, proactively mention how you'd test your solution with edge cases and scale it for large inputs.
Restate the problem in your own words, ask clarifying questions about input size, constraints, and edge cases, and confirm expected output format.
Discuss a brute-force solution first, then propose optimized approaches using suitable data structures (e.g., hash maps, heaps, two pointers) and explain trade-offs.
Outline the chosen algorithm step-by-step, analyze time and space complexity, and justify why it's optimal for the given constraints.
Write clean, modular code with meaningful variable names, then walk through test cases including edge cases to verify correctness.
Check for off-by-one errors, discuss potential improvements or alternative solutions, and mention how you'd handle scalability if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was more interesting than I expected.
Start by understanding the expected behavior and reproducing the bugs with test cases. Then systematically debug each issue, fix them, and verify with tests to ensure correctness.
Pro tip: Communicate your thought process clearly and prioritize fixes based on impact; show that you can balance thoroughness with time constraints.
Clarify the expected behavior of the code and identify what 'correct' means. Review any documentation or tests to understand the intended functionality.
Run the code with various inputs to reproduce the bugs. Use debugging tools and log statements to pinpoint the root causes of each issue.
Address bugs in order of severity or dependency, making minimal changes to fix each issue without introducing new problems.
After each fix, run tests to ensure the bug is resolved and no regressions occur. Add new tests if necessary to cover edge cases.
Review the changes for code quality and consider refactoring if the fixes are hacky. Ensure the solution is maintainable and efficient.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.