My first approach worked but was clearly not optimal.
First, solve the warm-up problem to establish a baseline solution and identify its limitations. Then, analyze how the extra constraint affects the problem, and adapt your approach by modifying the algorithm or using a different data structure. Discuss trade-offs and test with edge cases.
Pro tip: Demonstrate your thought process by explicitly connecting the warm-up solution to the harder version, showing how the extra constraint forces a change in approach. This highlights your ability to build on existing knowledge and adapt to new challenges.
Restate the problem in your own words, confirm the extra constraint, and ask clarifying questions about input/output, edge cases, and performance expectations.
Briefly explain the solution to the simpler version, including its time and space complexity, to establish a foundation.
Identify how the additional constraint invalidates or complicates the warm-up solution, and brainstorm potential modifications or alternative algorithms.
Develop an algorithm that handles the constraint, optimize it for time and space, and compare with the warm-up solution.
Walk through test cases, including edge cases, 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.
Spotted the approach fast and the interviewer told me to go ahead and code.
First, clarify the problem constraints and edge cases, then discuss the optimal approach with its time and space complexity. Implement the solution cleanly, test it with examples, and finally propose an alternative approach with trade-offs.
Pro tip: Always start by asking clarifying questions to ensure you understand the problem fully; this demonstrates thoroughness and can reveal hidden constraints that affect your solution.
Restate the problem in your own words and ask clarifying questions about input size, constraints, edge cases, and expected output.
Explain the optimal algorithm, including data structures used, and analyze its time and space complexity.
Write clean, modular code for the optimal solution, then walk through test cases including edge cases to verify correctness.
Describe an alternative algorithm, compare its trade-offs (e.g., time vs. space, simplicity vs. efficiency), and explain when it might be preferable.
Summarize the key points, discuss potential optimizations, and reflect on what you learned or would do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.