They wanted to hear the plan before any code went down.
Start by clarifying the problem, constraints, and edge cases with the interviewer, then outline a high-level algorithm and its complexity before writing any code. Walk through a concrete example to validate your approach, and only then implement the solution cleanly, explaining each step as you go.
Pro tip: Verbalize your thought process continuously, including trade-offs between different approaches, to demonstrate strong communication and problem-solving skills. If you get stuck, don't panic—ask clarifying questions or propose a brute-force solution first, then optimize.
Restate the problem in your own words, ask clarifying questions about input/output, constraints, and edge cases. Confirm assumptions with the interviewer before proceeding.
Brainstorm multiple solutions, from brute force to optimized, and discuss their time and space complexities. Choose the best approach based on constraints and explain your reasoning.
Pick a representative example (including edge cases) and manually step through your chosen algorithm to verify correctness and catch any logical flaws.
Write clean, modular code with meaningful variable names, explaining each part as you write. Handle edge cases and avoid premature optimization.
Test your code with the example and additional edge cases, debug if necessary, and discuss potential improvements or alternative solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
By the second question I was already feeling the clock.
Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures or algorithms. Verbally walk through your thought process, and once you have a clear plan, implement it cleanly with meaningful variable names and test with examples.
Pro tip: Since this is the second in a series, the interviewer may expect you to build on the first problem or reuse code. Explicitly mention any connections to the previous question and ask if you should extend your previous solution.
Ask questions to understand input/output formats, constraints, edge cases, and expected time/space complexity. Confirm any assumptions with the interviewer.
Propose a brute-force solution first, then analyze its complexity and suggest optimizations using appropriate data structures or algorithms. Compare trade-offs.
Write pseudocode or step-by-step logic to ensure the interviewer follows your plan. Get their buy-in before coding.
Write clean, modular code with clear variable names. Handle edge cases and avoid off-by-one errors. Comment on key steps.
Walk through test cases including normal, edge, and large inputs. Check for correctness and complexity. Discuss potential improvements if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem statement, constraints, and edge cases before diving into a solution. Discuss a brute-force approach first, then optimize using appropriate data structures and algorithms, analyzing time and space complexity. Write clean, modular code and test with examples.
Pro tip: Communicate your thought process continuously and show enthusiasm for solving the problem; interviewers value clear reasoning and collaboration over silent coding.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Brainstorm multiple solutions, starting with a brute-force method. Discuss trade-offs and identify the optimal approach based on time and space complexity.
Write clean, well-structured code with meaningful variable names. Explain your logic as you code and handle edge cases explicitly.
Walk through your code with sample inputs, including edge cases. If bugs are found, debug systematically and explain your fixes.
State the time and space complexity of your solution. Discuss potential optimizations or alternative approaches if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The negative numbers follow-up is what killed me.
First, clarify the original problem and your initial solution, then systematically analyze how negative numbers break assumptions (e.g., ordering, zero handling, overflow). Adapt the algorithm by adjusting comparisons, initial values, or data structures, and validate with edge cases like all negatives, mixed signs, and zeros.
Pro tip: Proactively discuss trade-offs between modifying the existing algorithm versus using a more general approach (e.g., prefix sums with a hash map for subarray problems), and mention potential pitfalls like integer overflow when negating values.
Restate the problem and explain your initial approach, highlighting any assumptions that rely on non-negative inputs.
Pinpoint specific steps (e.g., sorting, two-pointer, greedy choices) that fail with negative numbers and explain why.
Modify the approach to handle negatives, such as changing initial values, using absolute values, or switching to a more robust technique.
Walk through examples with all negatives, mixed signs, zeros, and extreme values to ensure correctness.
Discuss time/space complexity of the extended solution and compare with alternatives, noting any performance or readability trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.