They went pretty deep, not just surface-level stuff.
Select 2-3 recent projects that highlight AI integration and production readiness, and structure each using a lightweight STAR format. Focus on your specific contributions, technical decisions, and measurable impact, while weaving in trade-offs and lessons learned.
Pro tip: Amazon values customer obsession and ownership, so tie each project to a customer or business outcome and explicitly state what you would do differently next time to show growth.
Briefly state the project's goal, your role, and the team size to orient the interviewer.
Describe the architecture, key technologies (especially AI/ML components), and why you chose them over alternatives.
Discuss how you ensured scalability, reliability, and monitoring, and any production incidents you resolved.
Share metrics such as latency reduction, cost savings, or user engagement improvements to demonstrate business value.
Mention one key trade-off you made and what you learned, showing self-awareness and technical depth.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Misread the constraint at first and went down a completely wrong path for a few minutes.
This is a classic optimization problem that can be solved using binary search on the answer. The key is to define a feasible check: given a candidate number of candies per child, can we distribute that amount to all k children? Then binary search for the maximum feasible value.
Pro tip: Always clarify constraints (e.g., can candies be split? are piles indivisible?) and mention edge cases like k=0 or insufficient candies. Also, discuss time complexity and potential optimizations.
Restate the problem in your own words and ask clarifying questions. Confirm that each child must receive the same integer number of candies, and that candies from a pile cannot be split across children.
For a given target T, compute how many children can receive T candies by summing floor(pile_i / T) over all piles. If this sum is at least k, then T is feasible.
Binary search T in the range [0, max(pile_i)]. For each mid, check feasibility and adjust the search range accordingly to find the maximum feasible T.
Time complexity is O(n log M) where n is number of piles and M is the maximum pile size. Space complexity is O(1).
Consider cases where k=0 (return 0), total candies < k (return 0), or piles empty. Also, ensure integer overflow is handled if sums are large.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Started with DFS and immediately flagged the overlapping subproblems myself, then moved to memoization for O(MN) time and space.
First, clarify the problem constraints: grid dimensions, movement rules (only right to next column), and whether you can start at any cell in the first column. Then, model it as a dynamic programming problem where dp[col][row] represents the maximum moves ending at that cell, and compute the maximum over all cells.
Pro tip: Discuss both top-down memoization and bottom-up DP, and mention how to optimize space to O(rows) by only keeping the previous column's values. This shows you consider efficiency and scalability, which Amazon values.
Ask about grid size, movement constraints (only to next column, strictly greater value), and whether you can start at any cell in the first column. Confirm if moves are counted as steps taken.
Let dp[c][r] be the maximum number of moves to reach cell (c, r). Base case: dp[0][r] = 0 for all r in the first column.
For each cell (c, r) with c > 0, dp[c][r] = 1 + max(dp[c-1][r'] for all r' where grid[c-1][r'] < grid[c][r]). If no such r', dp[c][r] = 0 (or -inf if unreachable).
Iterate column by column, compute dp for each cell, and keep track of the global maximum moves. Return the maximum value found.
Time complexity is O(C * R^2) naively, but can be optimized to O(C * R log R) using sorting or segment trees. Space can be reduced to O(R) by storing only the previous column's dp values.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.