Passed all the test cases on this one so at least something went right.
Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures or algorithms. Walk through your thought process, write clean code, and test with examples to ensure correctness.
Pro tip: Always verbalize your reasoning and trade-offs; interviewers value clear communication and problem-solving skills over silent coding. If stuck, ask for hints or simplify the problem to make progress.
Restate the problem in your own words, ask clarifying questions about input/output, constraints, and edge cases. Confirm assumptions with the interviewer.
Discuss a brute-force solution first, then analyze its time and space complexity. Brainstorm optimizations using appropriate data structures or algorithmic paradigms.
Outline your chosen approach step-by-step, then write clean, modular code. Use meaningful variable names and handle edge cases explicitly.
Walk through your code with a few test cases, including edge cases. Identify and fix any bugs or logical errors.
State the final time and space complexity. Discuss potential improvements or alternative solutions if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the bug's symptoms and scope, then systematically narrow down the root cause using debugging tools and code reading. Propose a fix that addresses the root cause, considering trade-offs like performance, maintainability, and side effects, and validate with tests.
Pro tip: Demonstrate a methodical approach: reproduce the bug first, then use binary search or logging to isolate the faulty code. Mention that you'd add a regression test to prevent future occurrences.
Gather details about the bug: expected vs. actual behavior, environment, and steps to reproduce. Reproduce it locally to confirm.
Use debugging tools, logs, and code search to narrow down the source. Read related code and understand the intended behavior.
Analyze why the bug occurs: logic error, edge case, race condition, etc. Consider if it's a symptom of a deeper design issue.
Write a minimal fix that addresses the root cause. Add or update tests to cover the bug and ensure no regressions.
Document the fix, explain trade-offs, and communicate with the team. Consider if similar bugs exist elsewhere.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.