Model the grid as a graph where each cell is a node connected to its 4-directional neighbors. Use multi-source BFS starting from all initially rotten oranges simultaneously, tracking the time (BFS level) until no fresh oranges remain. Return the time if all fresh oranges rot, otherwise -1.
Pro tip: Clarify edge cases upfront (empty grid, no fresh oranges, no rotten oranges) and mention that the BFS queue processes level by level to compute the minimum time. Also, discuss space/time complexity: O(m*n) time and space.
Restate the problem: given a grid with fresh (1), rotten (2), and empty (0) cells, determine the minimum time for all fresh oranges to rot. Clarify that rotting spreads to adjacent fresh oranges each minute.
Recognize this as a shortest-path problem on an unweighted grid, best solved with multi-source BFS. Explain why BFS is optimal over DFS or simulation.
Enqueue all initially rotten oranges with time 0. Process the queue level by level, infecting adjacent fresh oranges and incrementing time. Track the number of fresh oranges to know when all are rotten.
After BFS, if any fresh oranges remain, return -1; otherwise return the elapsed time. Discuss edge cases like no fresh oranges (return 0) or no rotten oranges (return -1 if fresh exist).
State time complexity O(m*n) since each cell is processed once, and space complexity O(m*n) for the queue. Mention potential optimizations like in-place modification or using a 2D array for time.
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The interviewer was more interested in talking through trade-offs than seeing me type.
Model the courses and prerequisites as a directed graph and use topological sorting (Kahn's algorithm or DFS) to find a valid order. If a cycle exists, return an empty array to indicate impossibility. Explain the algorithm, analyze time and space complexity, and discuss trade-offs between BFS and DFS approaches.
Pro tip: At Amazon, emphasize how this algorithm scales for large dependency graphs and mention real-world applications like build systems or task scheduling. Also, proactively discuss handling edge cases such as disconnected graphs or duplicate prerequisites.
Ask about input format (e.g., number of courses, prerequisite pairs), output expectations (any valid order or specific order?), and constraints (e.g., course count up to 2000). Confirm if courses are labeled 0 to n-1.
Decide between Kahn's algorithm (BFS) and DFS-based topological sort. Explain why you chose one, e.g., Kahn's is intuitive for cycle detection and produces order naturally.
Describe building the graph (adjacency list) and in-degree array. Then outline the BFS process: enqueue nodes with in-degree 0, process neighbors, decrement in-degrees, and enqueue when in-degree becomes 0. Track processed count to detect cycles.
State time complexity O(V+E) and space O(V+E). Discuss edge cases: no prerequisites, cycle (return empty array), disconnected components, and duplicate edges.
Compare BFS vs DFS: BFS is iterative and avoids recursion depth issues; DFS can be more concise but may need cycle detection with colors. Mention potential optimizations like using arrays instead of queues for small graphs.
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DP problems with weird shapes always make me nervous and this one was no exception.
Define a recurrence relation by considering the rightmost column and the possible placements of dominoes and trominoes, then derive a closed-form or iterative DP solution. Explain the base cases and how to compute the answer modulo 10^9+7 for large N.
Pro tip: Mention that this is a classic dynamic programming problem (similar to LeetCode 790) and that you can optimize space to O(1) by keeping only the last few states. Also, clarify whether the board is fixed at 2 rows and N columns, and whether rotations of trominoes are allowed.
Confirm the board dimensions (2xN), the shapes (1x2 domino and L-tromino), and whether rotations/reflections are allowed. Ask if N can be large and if modulo is required.
Let f(n) be the number of ways to tile a 2xn board. Consider the rightmost column: it can be filled by a vertical domino, two horizontal dominoes, or an L-tromino plus a horizontal domino. Derive f(n) = 2*f(n-1) + f(n-3) for n>=3, with base cases f(0)=1, f(1)=1, f(2)=2.
Compute f(0) to f(4) manually to ensure the recurrence holds. For example, f(3)=5, f(4)=11.
Use dynamic programming with O(N) time and O(1) space by keeping only the last three values. If N is very large, consider matrix exponentiation for O(log N) time.
State time and space complexity. Handle N=0 (return 1) and modulo operations if required. Mention that the recurrence can be derived via generating functions or combinatorial arguments.
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Talked about using it for boilerplate and debugging mostly.
Structure your answer around specific AI tools you use daily (e.g., GitHub Copilot, ChatGPT) and the concrete tasks they accelerate, then pivot to a limitation you've encountered and how you adapted. Emphasize that you treat AI as a productivity multiplier, not a replacement for engineering judgment, and that you always validate its output.
Pro tip: Amazon values customer obsession and ownership—frame AI usage in terms of how it helps you deliver better results for customers faster, and show you're aware of risks like security, bias, and technical debt. Mention that you've set up guardrails (e.g., never pasting sensitive code) to demonstrate ownership.
Briefly list the AI tools you use regularly (e.g., GitHub Copilot, ChatGPT, Amazon CodeWhisperer) and the specific tasks they help with, such as code generation, debugging, or documentation.
Share a concrete example of how AI improved your productivity—e.g., reduced boilerplate coding time by 30% or helped you learn a new framework faster—to show tangible value.
Pick one meaningful limitation (e.g., hallucinated APIs, outdated knowledge, security concerns) and explain the context: what you were doing, what went wrong, and how you noticed.
Detail how you adapted: e.g., always verifying AI-generated code with tests, using AI only for non-sensitive tasks, or combining AI suggestions with manual review.
Tie your approach back to Amazon Leadership Principles like Customer Obsession, Ownership, and Invent & Simplify—showing you use AI to innovate while maintaining high standards.
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This is the kind of question where knowing the theory isn't enough, you need to have thought about it practically.
Start by explaining the root causes of hallucinations in LLMs, covering both model and data factors. Then, outline a multi-layered validation strategy that combines automated checks, human review, and continuous monitoring, tailored to the use case's risk profile. Emphasize the importance of grounding, evaluation metrics, and iterative improvement.
Pro tip: Tie your answer to Amazon's leadership principles, such as 'Customer Obsession' and 'Deliver Results', by highlighting how validation ensures customer trust and business impact. Also, mention specific AWS services like Bedrock Guardrails or SageMaker Clarify to show practical knowledge.
Discuss why LLMs hallucinate: training data limitations (bias, gaps, outdated info), model architecture (next-token prediction, lack of factual grounding), and inference parameters (temperature, top-k).
Outline a multi-layered validation approach: automated fact-checking against knowledge bases, consistency checks, and confidence scoring; human-in-the-loop for high-stakes outputs; and user feedback loops.
Mention metrics like factual accuracy, hallucination rate, and relevance, and how to measure them using benchmarks (e.g., TruthfulQA) or custom datasets.
Address trade-offs between validation rigor and latency/cost, and how to design for scalability and real-time constraints.
Explain how to use validation results to fine-tune models, update knowledge bases, and implement guardrails for production.
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Covered chain-of-thought, few-shot examples, and role prompting.
Start by defining prompt engineering in the context of LLMs, emphasizing its role in optimizing model outputs for specific tasks. Then, discuss techniques you've applied, linking them to software engineering principles like iteration, testing, and trade-offs. Finally, highlight how these techniques can be leveraged at Amazon to solve customer problems and improve system efficiency.
Pro tip: Demonstrate a growth mindset by acknowledging the evolving nature of prompt engineering and expressing eagerness to learn from Amazon's scale and data. Avoid claiming expertise in every technique; instead, focus on depth in a few and show how you quickly adapt.
Provide a clear, concise definition that shows you understand its purpose: designing inputs to guide LLMs toward desired outputs. Mention that it's both an art and a science, requiring experimentation and systematic evaluation.
Connect prompt engineering to business impact: improving accuracy, reducing costs, and enabling new capabilities. For Amazon, relate it to customer obsession, operational excellence, and innovation.
Describe 2-3 techniques you've used or know well, such as few-shot prompting, chain-of-thought, or role prompting. For each, briefly explain how it works and when to use it.
Highlight the importance of measuring prompt effectiveness and iterating. Mention trade-offs like prompt length vs. cost, specificity vs. flexibility, and how you've balanced them.
Tie your answer back to Amazon's Leadership Principles (e.g., Customer Obsession, Invent and Simplify) and express enthusiasm for applying prompt engineering to solve real-world problems at scale.
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Time was short because the interviewer joined late, so I explained the algorithm instead of writing full code.
First, clarify that the input is a sequence of digits (e.g., an array or string) and that we need the next lexicographically greater permutation, or the smallest permutation if none exists. Then, describe the standard algorithm: find the rightmost digit that is smaller than its successor, swap it with the smallest larger digit to its right, and reverse the suffix. Finally, analyze time and space complexity and discuss edge cases.
Pro tip: Mention that this is the same algorithm used in C++'s std::next_permutation and that it works in-place with O(1) extra space, which is important for large inputs. Also, proactively discuss how to handle duplicates and the case where the number is already the largest permutation.
Confirm that the input is a sequence of digits (e.g., integer, string, or array) and that we need the next lexicographic permutation. Ask about duplicates, negative numbers, and whether the result should be returned as the same type.
Scan from right to left to find the first digit that is smaller than its right neighbor. This digit is the pivot; if no such digit exists, the sequence is in descending order, so the next permutation is the sorted ascending order.
From the right, find the smallest digit that is larger than the pivot, then swap it with the pivot. This ensures the smallest possible increase at the pivot position.
Reverse the subarray to the right of the pivot to arrange it in ascending order, which gives the smallest possible permutation for the new prefix.
State that the algorithm runs in O(n) time and O(1) extra space. Discuss edge cases: single digit, all digits same, descending order, and handling duplicates.
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Used a situation from my current job where I picked up a half-finished feature nobody wanted to touch.
Use the STAR method to describe a situation where you voluntarily took on responsibilities beyond your assigned role, focusing on the actions you took and the impact you made. Emphasize how you identified a gap, took initiative, and drove results that benefited the team or company. Highlight your ownership mindset and ability to deliver under ambiguity.
Pro tip: Quantify the impact of your ownership with specific metrics (e.g., reduced latency by 30%, saved $50K annually) to make your story memorable and demonstrate Amazon's bias for action. Also, show how you influenced others or scaled your solution, aligning with Amazon's leadership principles like Ownership and Deliver Results.
Briefly describe the project or problem, your role, and why it was important. Mention that it was outside your expected responsibilities.
Explain how you recognized the need to take ownership—what was missing or at risk—and why you decided to step up.
Detail the specific steps you took to address the problem, including any challenges you overcame and how you collaborated with others.
Share the outcomes of your efforts, using quantifiable metrics if possible, and explain the impact on the team, project, or company.
Summarize what you learned and how it demonstrates your ownership mindset, linking back to Amazon's leadership principles.
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Pulled out a debugging story from a performance issue I'd investigated.
Use the STAR method to structure your answer, focusing on the depth of your investigation and the tools/techniques you used. Emphasize how you systematically eliminated hypotheses and validated the root cause. Conclude with the impact of your fix and what you learned.
Pro tip: Amazon values 'Dive Deep' and 'Learn and Be Curious'. Show how your deep dive not only fixed the immediate issue but also prevented future occurrences or improved system resilience.
Briefly describe the situation and the technical problem, including its impact on users or the business. Mention why it was challenging and why a superficial fix wasn't sufficient.
Explain the steps you took to investigate, such as analyzing logs, using debugging tools, forming and testing hypotheses, and collaborating with others. Highlight your systematic approach.
Describe the root cause you discovered, including any technical details that demonstrate your depth of understanding. Explain how you validated it was the true cause.
Explain the solution you implemented, how you tested it, and how you ensured it resolved the issue without introducing new problems.
Summarize the results, including metrics if possible, and what you learned from the experience. Mention any preventive measures or improvements made to avoid similar issues.
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This was the round that probably cost me the offer.
Start by clarifying the problem constraints and edge cases, then propose an efficient elimination-based algorithm that reduces the candidate set in O(n) time. Explain the two-pass approach: first find a potential celebrity, then verify their status. Discuss trade-offs between time and space complexity, and mention how this scales for large datasets.
Pro tip: Emphasize the elimination strategy: each comparison eliminates one person, so you can find the celebrity in O(n) time without extra space. This shows you can optimize beyond brute force and handle Amazon-scale data.
Ask about input format, size, and whether the 'knows' relation is symmetric or if there can be zero or multiple celebrities. Confirm expected time/space complexity.
Describe how to maintain a candidate and eliminate one person per comparison: if A knows B, A cannot be celebrity; else B cannot be. This reduces candidates to one in O(n) steps.
After elimination, verify the remaining candidate by checking that everyone knows them and they know no one. This takes O(n) additional checks.
State time complexity O(n) and space O(1). Compare with brute force O(n^2) and discuss when brute force might be acceptable for small n.
Discuss cases like no celebrity, multiple celebrities, or incomplete data. Mention how to adapt if the 'knows' matrix is sparse or if you need to find all celebrities.
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