Went in expecting graphs or DP and got completely blindsided.
First, clarify the problem and constraints, then identify how two pointers and prefix sums can be combined to achieve an efficient solution. Walk through the algorithm step-by-step, explicitly addressing boundary conditions and edge cases, and analyze time and space complexity.
Pro tip: At Amazon, interviewers value candidates who proactively discuss trade-offs and test their solution with edge cases like empty arrays, single elements, and extreme values. Always mention how you would handle these before being asked.
Ask questions to understand the input format (array or matrix), expected output, and any constraints on time/space. Confirm whether the array can be empty, contain negative numbers, or have duplicate values.
Determine how two pointers can maintain a window or traverse the structure, and how prefix sums can quickly compute subarray sums. Consider if the problem involves finding subarrays with a target sum, maximum sum, or partitioning.
Outline the steps: initialize pointers and prefix sum array, iterate while moving pointers, and update results. Explicitly handle boundaries such as start/end of array, empty input, and out-of-bounds indices.
Trace the algorithm on a small example, then test edge cases: empty array, single element, all negative numbers, target sum not present, and large input. Verify that boundary conditions are correctly managed.
State the time and space complexity (typically O(n) time and O(n) space for prefix sums). Discuss potential optimizations, such as using a hash map to store prefix sums for O(1) lookups, and trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one hurt more than the coding question.
Start by thoroughly reading the problem statement and understanding the expected behavior from the test cases. Then systematically debug the code by reproducing failures, isolating the root cause, and applying a minimal fix. Finally, verify the fix against all test cases and consider edge cases and time/space complexity.
Pro tip: Talk through your debugging process out loud, explaining your hypotheses and how you validate them. This demonstrates structured problem-solving and communication skills, which Amazon values highly.
Read the problem statement carefully and examine the provided test cases to infer expected inputs, outputs, and edge cases. Clarify any ambiguities before diving into the code.
Run the code against the test cases to see failures. Use debugging tools (breakpoints, print statements) to trace execution and identify where actual behavior diverges from expected.
Determine why the bug occurs—e.g., off-by-one error, incorrect data structure usage, missing edge case handling. Consider algorithmic and logical flaws, not just syntax.
Apply a minimal, targeted fix that addresses the root cause without introducing regressions. Re-run all test cases to confirm the fix works and consider additional edge cases.
Check if the fix affects time/space complexity or code readability. If needed, refactor for clarity or efficiency, ensuring the solution remains correct.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
For behavioral/situational multiple choice questions, focus on selecting the option that best aligns with Amazon's Leadership Principles, especially Customer Obsession and Ownership. Read each scenario carefully, identify the core conflict or ambiguity, and choose the response that demonstrates proactive problem-solving, data-driven decision-making, and bias for action while maintaining high standards.
Pro tip: Amazon values decisions that balance speed with calculated risk; when in doubt, choose the option that shows you take ownership, seek diverse perspectives, and deliver results without over-analyzing. Avoid options that are passive, blame others, or ignore long-term impact.
Determine the main challenge in the scenario, such as conflicting priorities, incomplete information, or stakeholder disagreement.
Recall relevant principles like Customer Obsession, Ownership, Bias for Action, and Deliver Results to guide your choice.
Assess each option's potential to solve the problem effectively, considering short-term and long-term consequences for customers and the team.
Select the option that demonstrates initiative, data-driven reasoning, and alignment with Amazon's culture of innovation and customer focus.
Ensure your choice doesn't violate any principles, such as sacrificing long-term value for short-term gains or ignoring team input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.