Aced both with full test case scores, so no complaints there.
For each coding problem, start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing it using appropriate data structures and algorithms. Write clean, modular code with meaningful variable names and test it with sample inputs, explaining your thought process throughout.
Pro tip: Amazon values customer obsession and ownership, so as you code, verbalize how your solution scales and handles edge cases, and mention potential trade-offs between time and space complexity. Also, practice writing code on a shared editor without syntax errors, as Amazon's online assessment often uses platforms like HackerRank or CodeSignal.
Restate the problem in your own words, ask clarifying questions about input size, constraints, and expected output, and confirm edge cases.
Propose a brute-force solution first, then analyze its time and space complexity and suggest optimizations using appropriate data structures or algorithms.
Write clean, well-structured code with clear variable names and comments, breaking down the logic into small functions if helpful.
Walk through your code with sample inputs, including edge cases, and fix any bugs or logical errors you find.
State the final time and space complexity, and discuss any trade-offs or potential improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty self-explanatory once you realize it's basically just the leadership principles dressed up in scenario form.
Use the STAR method to structure your response, focusing on a specific situation where you navigated ambiguity or adapted to change. Explicitly connect your actions to Amazon's Leadership Principles, such as 'Customer Obsession' or 'Bias for Action', and highlight the positive outcome.
Pro tip: Amazon values data-driven decisions; quantify your results whenever possible to demonstrate impact. Also, show self-awareness by briefly reflecting on what you learned and how you would apply it in the future.
Briefly describe the situation, emphasizing the ambiguity or need for adaptability. Mention the project, team, and your role.
Detail the steps you took to navigate the ambiguity, such as gathering data, consulting stakeholders, or making a decision with incomplete information. Highlight how you embodied Amazon's Leadership Principles.
Describe the results of your actions, using metrics if possible. Explain how your adaptability led to a successful outcome for the customer or team.
Share what you learned from the experience and how it has influenced your approach to similar situations since then.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.