Got the core implementation down fine but the follow-up questions on design patterns felt a bit more open-ended than I expected.
Start by clarifying requirements and constraints, then explain the standard hash map + doubly linked list implementation for O(1) operations. Discuss object-oriented design principles and relevant design patterns, and finally analyze trade-offs and potential extensions.
Pro tip: Mention that while the classic implementation uses a doubly linked list, you could also use an ordered dictionary (like LinkedHashMap in Java) to simplify, but be prepared to explain the underlying mechanics. Also, discuss thread-safety and how you might handle concurrency in a real system.
Ask about expected operations (get, put), capacity constraints, and whether thread-safety is required. Confirm that O(1) time complexity is desired.
Explain the combination of a hash map for O(1) access and a doubly linked list to maintain recency order. Describe how nodes are moved to the front on access and evicted from the tail when capacity is exceeded.
Identify key classes: LRUCache, Node, and possibly an interface for eviction policies. Discuss encapsulation, single responsibility, and how to make the design extensible.
Mention patterns like Strategy (for interchangeable eviction policies), Factory (for creating cache instances), and Singleton (if a global cache is needed). Explain how they apply.
Discuss time/space complexity, concurrency considerations (e.g., using locks or concurrent data structures), and possible enhancements like TTL or persistence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one surprised me a bit, didn't expect it in a technical round.
Choose a project where you integrated AI/ML to solve a real problem, and structure your answer using a clear framework like STAR. Focus on the trade-offs you considered, how you handled ambiguity, and the measurable impact of your solution, while showing you can learn and adapt quickly.
Pro tip: Amazon values customer obsession and bias for action; emphasize how the AI/ML solution directly benefited the customer or business, and be honest about limitations and what you'd do differently next time.
Briefly describe the project, your role, and the problem that motivated using AI/ML. Keep it concise and relevant to the role.
Detail how you selected the model, data sources, and tools, and why you chose that approach over alternatives. Highlight any experimentation or iteration.
Talk about the trade-offs you made (e.g., accuracy vs. latency, cost vs. performance) and how you navigated ambiguity or obstacles.
Share measurable results (e.g., improved accuracy, reduced costs, time saved) and how the solution benefited the customer or business.
Summarize key lessons learned and how you would improve the approach in the future, showing growth and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to structure your answer, focusing on a specific technical challenge that required you to navigate ambiguity and adapt. Highlight how you broke down the problem, took ownership, and delivered results aligned with Amazon's Leadership Principles.
Pro tip: Choose a story where you initially struggled or failed, then turned it around—this demonstrates humility and resilience, which Amazon values highly. Quantify the impact to show the scale of the difficulty and your success.
Briefly describe the situation, including the project, team, and why it was difficult. Mention any ambiguity, constraints, or high stakes involved.
Clearly state the specific technical or organizational obstacle you faced. Explain why it was particularly difficult and what made it non-trivial.
Walk through the steps you took to address the challenge. Emphasize your thought process, decisions, and how you adapted to changing circumstances.
Describe how you worked with others, sought input, or led the effort. Show how you embodied Amazon's Leadership Principles like Customer Obsession, Ownership, and Bias for Action.
Quantify the outcome (e.g., performance improvements, cost savings) and reflect on what you learned. Connect it to your growth as an engineer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Picked a story where the feedback was actually pretty fair in hindsight, which I think helped make the answer feel genuine.
Choose a specific instance where you received constructive criticism, ideally related to a technical or collaborative aspect of software engineering. Use the STAR method to describe the situation, your initial reaction, the actions you took to address the feedback, and the positive outcome. Emphasize your growth mindset and how you applied the feedback to improve your performance.
Pro tip: Show that you not only accepted the feedback but also took initiative to prevent similar issues, such as creating a checklist or sharing learnings with the team. This demonstrates ownership and a bias for action, which are highly valued at Amazon.
Briefly describe the situation and the feedback you received, including who gave it and why it was important. Be specific about the project or task.
Acknowledge any initial emotions (e.g., surprise, defensiveness) but emphasize that you quickly shifted to a constructive mindset. Avoid sounding defensive or dismissive.
Detail the steps you took to understand and address the feedback, such as asking clarifying questions, seeking mentorship, or implementing changes. Highlight your proactive approach.
Describe the positive results of your actions, such as improved code quality, better team collaboration, or personal growth. Quantify if possible.
Summarize what you learned and how you've applied it to future situations. Show that you continuously seek feedback and strive for improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.