This part went fine, maybe the best part of round 1 for me.
Choose a project where you navigated significant ambiguity or changing requirements, and structure your answer using the STAR method. Focus on your specific contributions, the technical decisions you made, and the measurable impact on the customer or business.
Pro tip: Amazon values Ownership and Customer Obsession—frame your project around a customer problem and highlight times you took initiative beyond your assigned tasks. Quantify results with metrics (e.g., latency reduction, cost savings) to demonstrate tangible impact.
Briefly describe the project's purpose, the customer problem it solved, and the team structure. Keep it concise to leave time for your actions and results.
Explain what was unclear at the start—such as undefined requirements, technical unknowns, or shifting priorities—and why it was challenging.
Walk through the specific steps you took to drive the project forward, including how you made decisions, collaborated with others, and adapted to changes.
Quantify the outcomes with metrics (e.g., performance improvements, cost savings, user adoption) and explain the impact on the customer and business.
Summarize what you learned and how you applied those lessons to future projects, showing growth and self-awareness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use the STAR method to describe a specific situation where you prioritized customer needs, focusing on your actions and the measurable impact. Emphasize how you balanced technical constraints with customer obsession, and tie it to Amazon's Leadership Principles.
Pro tip: Quantify the customer impact (e.g., reduced latency by 40%, increased customer satisfaction by 20%) to demonstrate that your focus on the customer delivered tangible results. Also, explicitly mention how you sought customer feedback or data to validate your decisions.
Briefly describe the project, the customer (internal or external), and the specific customer pain point or requirement you addressed.
Detail the technical or business constraints that made it difficult to meet the customer's needs, such as tight deadlines, legacy systems, or conflicting priorities.
Outline the steps you took to advocate for the customer, including any data gathering, cross-team collaboration, or innovative solutions you implemented.
Share the measurable results of your actions, such as improved customer satisfaction, reduced support tickets, or increased adoption.
Summarize what you learned and explicitly connect your actions to Amazon's Customer Obsession Leadership Principle.
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 technical disagreement that impacted the project. Emphasize how you listened to the other person's perspective, used data to make decisions, and maintained a professional relationship. Highlight the positive outcome and what you learned about collaboration.
Pro tip: Choose a conflict where you were not entirely right; showing humility and willingness to change your mind demonstrates maturity and aligns with Amazon's 'Have Backbone; Disagree and Commit' principle.
Briefly describe the project, your role, and the teammate involved. Keep it concise to focus on the conflict.
Clearly state the disagreement, such as differing technical approaches or priorities. Avoid blaming; focus on the issue.
Detail how you addressed the conflict: listening, seeking data, proposing compromises, or escalating appropriately.
Explain how the conflict was resolved, whether through consensus, data-driven decision, or manager input. Mention the outcome.
Summarize what you learned and how it improved your teamwork or technical judgment. Connect to Amazon's Leadership Principles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one surprised me a little, didn't expect it to come up so directly.
Start by defining generative AI and RAG concisely, then connect them to a real project or scenario where you applied or evaluated these technologies. Emphasize trade-offs and system design considerations, aligning with Amazon's leadership principles like Customer Obsession and Invent and Simplify.
Pro tip: Show you understand that RAG is not just about retrieval but about grounding LLM outputs to reduce hallucinations and improve accuracy, and mention how you'd measure success (e.g., relevance, latency, cost).
Briefly explain generative AI (models that create new content) and RAG (augmenting LLMs with external knowledge retrieval). Keep it crisp to show foundational knowledge.
Share a specific example where you worked with or thought about these technologies, focusing on your role and the problem you solved.
Discuss key trade-offs such as accuracy vs. latency, cost of fine-tuning vs. RAG, and when to use each approach.
Explain how you would architect a RAG system, including components like vector databases, embedding models, and orchestration.
Tie your answer to Amazon's scale, customer focus, and potential use cases (e.g., product Q&A, customer service automation).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by restating the problem and clarifying constraints (e.g., graph size, edge weights, directed/undirected). Then, think aloud to identify the key observation that transforms the problem into a known graph traversal technique (e.g., BFS/DFS with state, topological sort, or union-find). Finally, outline the algorithm, analyze time/space complexity, and discuss edge cases and potential optimizations.
Pro tip: Demonstrate Amazon's Leadership Principles by proactively discussing trade-offs (e.g., between BFS and DFS) and how your solution scales for large inputs, showing customer obsession and ownership.
Ask clarifying questions to fully grasp the problem: input format, output requirements, constraints, and edge cases. Confirm your understanding with the interviewer.
Analyze the problem to find the non-obvious insight that simplifies it. This might involve recognizing a pattern, reducing to a known problem, or using a specific data structure.
Choose an appropriate graph traversal technique (BFS, DFS, topological sort, etc.) and outline the steps. Consider using auxiliary data structures like queues, stacks, or sets.
Calculate time and space complexity, and discuss how the algorithm handles edge cases (e.g., disconnected graphs, cycles, large inputs).
Write clean, modular code and walk through a small example to verify correctness. Be prepared to optimize if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.