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They stopped me pretty early and asked me to just talk through what I was thinking before writing anything.
Narrate your problem-solving process in a clear, step-by-step manner, starting with restating the problem and clarifying constraints. Emphasize trade-offs and decision points, and conclude by discussing testing and edge cases.
Pro tip: Show that you think about scalability and real-world constraints from the start—Amazon values engineers who consider how solutions perform at scale and under failure.
Restate the problem in your own words and ask clarifying questions about inputs, outputs, constraints, and edge cases to ensure full understanding.
Brainstorm multiple algorithms or data structures, discussing their time and space complexities and trade-offs.
Choose the best approach based on constraints and explain why it’s optimal, mentioning any assumptions.
Describe the steps to implement the solution, including key data structures and control flow, without writing full code.
Walk through testing the solution with normal and edge cases, and discuss how you would handle failures or optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Second round threw a wrinkle at my original approach and I had to pivot mid-solution.
Restate the new constraint clearly, then explain how it affects your original solution's correctness, time/space complexity, and edge cases. Walk through a concrete adjustment—such as changing the data structure, algorithm, or trade-off—and validate it against the constraint with a quick example or complexity analysis.
Pro tip: Frame the adjustment as a deliberate trade-off: acknowledge what you gain (e.g., lower latency, less memory) and what you give up (e.g., extra preprocessing, higher write cost), which signals engineering maturity to AWS interviewers.
Repeat the follow-up constraint in your own words and confirm assumptions (e.g., input size, latency budget, memory limits) before proposing changes.
Identify which parts of your current approach break or degrade—correctness, time complexity, space complexity, or scalability—under the new constraint.
Describe a specific modification (e.g., swap a hash map for a sorted array, add caching, use a heap, stream instead of store) and explain why it addresses the constraint.
Compare the adjusted solution to the original in terms of time/space complexity, code complexity, and operational cost, and state which trade-off you accept and why.
Walk through a small example or edge case to show the adjusted solution works and meets the constraint, then summarize the final approach.
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This is where I felt most comfortable and most exposed at the same time.
Choose one or two key architectural decisions from a project you know deeply, and walk through them using a structured narrative: context, options considered, decision criteria, trade-offs, and outcome. Emphasize why the chosen technologies fit the requirements and constraints, and quantify impact where possible.
Pro tip: Frame decisions as trade-offs rather than absolute right choices, and explicitly mention what you gave up—this shows architectural maturity and aligns with Amazon's emphasis on trade-off analysis.
Briefly describe the project's goals, scale, and constraints (e.g., latency, cost, team size) so the interviewer understands the decision environment.
Clearly name the architectural decision and list the main alternatives you considered, showing you evaluated multiple options.
Describe the factors that drove your choice—such as scalability, operational overhead, team expertise, or time-to-market—and how you weighted them.
Acknowledge what you sacrificed with the chosen technology and how you mitigated potential downsides.
Quantify the impact (e.g., performance gains, cost savings) and reflect on what you learned or would do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Didn't expect this to go as deep as it did.
Start by defining what hallucinations meant in your GenAI project and their impact, then walk through a specific example using a structured problem-solving narrative. Highlight the root causes you identified and the trade-offs you made when implementing mitigation strategies, emphasizing measurable outcomes.
Pro tip: Quantify the reduction in hallucinations (e.g., 'reduced from 15% to 3%') and mention how you balanced mitigation with cost and latency—AWS values pragmatic engineering that considers operational impact.
Briefly describe the GenAI application, its purpose, and why hallucinations were a critical issue (e.g., customer trust, compliance).
Explain how you diagnosed the sources of hallucinations, such as ambiguous prompts, lack of grounding data, or model limitations.
Detail the approaches you implemented, such as retrieval-augmented generation (RAG), prompt engineering, fine-tuning, or output validation.
Highlight the trade-offs you considered, like latency vs. accuracy, cost of additional API calls, or complexity of maintaining a knowledge base.
Quantify the improvement (e.g., reduced hallucination rate) and reflect on what you learned or would do differently.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a structured narrative like STAR to describe the project, then explicitly discuss trade-offs, challenges, and results. End with a reflective 'what I'd change' that shows growth and learning.
Pro tip: Frame trade-offs as deliberate decisions with clear rationale, and tie your 'what I'd change' to a concrete lesson that you've already applied elsewhere—this shows self-awareness and continuous improvement.
Summarize the project's goal, your role, and the scale or impact in 1-2 sentences to ground the listener.
Describe 1-2 major trade-offs you made (e.g., consistency vs. availability, build vs. buy) and why you chose one over the other.
Pick a significant technical or organizational challenge, explain its impact, and detail your solution and the outcome.
Share measurable outcomes (e.g., latency reduction, cost savings, adoption metrics) to demonstrate impact.
Identify one or two things you'd do differently now, explain why, and mention how you've applied that lesson since.
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 identified a gap outside your assigned scope, took initiative to address it, and delivered measurable impact. Emphasize your decision-making process, collaboration with stakeholders, and the results achieved, highlighting Amazon's Leadership Principles like Ownership and Bias for Action.
Pro tip: Choose an example where you influenced without authority and connected your actions to customer or business impact—Amazon values 'Ownership' as thinking long-term and not saying 'that's not my job.'
Briefly describe the project, your role, and the gap or issue you noticed that was outside your formal responsibilities.
Share your thought process: why you decided to take ownership, what risks you considered, and how you aligned with stakeholders.
Detail the concrete steps you took—how you collaborated, communicated, and executed the work while managing your existing responsibilities.
Quantify the results: time saved, revenue impact, customer satisfaction, or team efficiency. Mention any recognition or lasting process improvements.
Summarize what you learned and tie it back to Amazon's Leadership Principles, showing how this mindset drives success at AWS.
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Told a real story about a bug I shipped that caused downstream issues.
Use the STAR method to describe a specific mistake, focusing on the root cause analysis and the corrective actions taken. Emphasize what you learned and how you implemented changes to prevent recurrence, aligning with AWS's emphasis on ownership and continuous improvement.
Pro tip: Choose a mistake that had real impact but was resolved successfully, and highlight the systemic fix you implemented—this shows you think beyond quick fixes and drive long-term improvements.
Briefly describe the project, your role, and the situation to give the interviewer enough background to understand the mistake's significance.
Clearly state what went wrong, its impact, and your immediate reaction—own it without deflecting blame.
Explain how you investigated to find the underlying cause, using tools like the '5 Whys' or fishbone diagram, and share your findings.
Describe the steps you took to fix the issue and prevent recurrence, including any process improvements or automation you introduced.
Summarize what you learned and how it changed your approach, emphasizing personal growth and benefits to the team.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: pick a real conflict, not a fake one where everyone ends up happy and learns a lesson.
Use the STAR method to structure your answer, focusing on a specific conflict you resolved through empathy, data-driven discussion, and collaboration. Highlight how you listened to the other party's perspective, communicated your own viewpoint clearly, and worked together to find a solution that benefited the team and the project.
Pro tip: Emphasize that you sought to understand the other person's perspective first and used data or objective criteria to resolve the disagreement, showing maturity and alignment with Amazon's Leadership Principles like 'Have Backbone; Disagree and Commit' and 'Earn Trust'.
Briefly describe the situation and the conflict, including the parties involved and the stakes. Keep it concise to focus on your actions.
Detail the steps you took to address the conflict, such as setting up a private conversation, actively listening, and seeking to understand the other person's viewpoint.
Explain how you collaborated to find a mutually agreeable solution, possibly using data or compromise, and how you ensured buy-in from all parties.
Share the positive results of the resolution, such as improved team dynamics, project success, or strengthened relationships, and what you learned.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had a decent story here about picking up a new framework under time pressure.
Use the STAR method to structure your answer, focusing on a specific instance where you rapidly acquired a new skill or technology under time pressure. Emphasize the concrete steps you took to learn efficiently, the resources you leveraged, and the successful outcome that met or exceeded the deadline.
Pro tip: Highlight how you prioritized the most critical aspects of the new technology to deliver value quickly, and mention any trade-offs you made—this shows pragmatic decision-making valued at AWS.
Briefly describe the situation: the project, the deadline, and why learning something new was necessary. Mention the stakes and any constraints.
Detail the specific approach you took to learn quickly: what resources you used (documentation, tutorials, mentors), how you prioritized topics, and how you balanced learning with doing.
Explain how you applied the new knowledge to the project, including any challenges you faced and how you overcame them. Emphasize iterative learning and quick adjustments.
Mention how you sought help from colleagues, used internal tools, or leveraged community resources to accelerate your learning and avoid pitfalls.
Conclude with the outcome: did you meet the deadline? What was the impact? Reflect on what you learned about rapid learning and how you might apply it in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.