This came before the loop, in the phone screen, which I wasn't really expecting.
Choose a low-level design problem that directly relates to the role's domain (e.g., a rate limiter, a parking lot system, or a URL shortener) and walk through it step by step. Start by clarifying requirements and constraints, then dive into class/interface design, data structures, and algorithms, explaining trade-offs at each decision point. Conclude by discussing how you would test, scale, and maintain the solution.
Pro tip: Amazon values customer obsession and ownership, so frame your design decisions around customer impact and long-term maintainability. Explicitly call out trade-offs (e.g., consistency vs. availability, memory vs. speed) and justify your choices with data or real-world examples.
Ask clarifying questions to understand functional and non-functional requirements (e.g., expected scale, latency, consistency needs). Define the core use cases and constraints before designing.
Identify the main entities, their responsibilities, and interactions. Sketch class diagrams or interfaces, focusing on separation of concerns and extensibility.
Choose appropriate data structures (e.g., hash maps, heaps, tries) and algorithms to meet performance goals. Explain time/space complexity and trade-offs.
Discuss how the design handles concurrent access, scaling (e.g., sharding, caching), and failure scenarios. Mention locking, partitioning, or replication as needed.
Outline unit, integration, and load testing strategies. Explain how you would monitor, debug, and iterate on the design based on metrics and feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Structure your answer around 2-3 specific projects where you applied AI/ML, emphasizing your role, the technical decisions you made, and the measurable impact. Highlight how you navigated ambiguity and balanced trade-offs, aligning with Amazon's leadership principles and the role's focus.
Pro tip: Quantify your impact with metrics (e.g., accuracy improvement, latency reduction, cost savings) and explicitly connect your experience to Amazon's scale and customer obsession. Show that you understand not just the tech, but also the business value.
Briefly introduce the project, your role, and the business problem you aimed to solve with AI/ML. Keep it concise to focus on your actions.
Describe the ML models, algorithms, or AI techniques you used, and why you chose them. Mention any trade-offs you considered (e.g., accuracy vs. latency, complexity vs. maintainability).
Discuss a specific challenge (e.g., data quality, unclear requirements) and how you navigated it. Show adaptability and problem-solving.
Share measurable results (e.g., improved accuracy by X%, reduced costs by Y%, increased customer engagement). Tie back to business outcomes.
Relate your experience to Amazon's scale, customer obsession, and leadership principles. Mention how you could apply these skills to the role.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They pushed on this harder than I expected for a consultant-track role.
Start by briefly summarizing your overall AWS experience, then dive into 1-2 specific projects where you used AWS services to solve real problems. Focus on the services you used, the architectural decisions you made, and the impact of your work, emphasizing trade-offs and integration challenges.
Pro tip: Amazon values customer obsession and ownership, so frame your AWS experience in terms of how it benefited the end user or business, and highlight times you took ownership of a problem and made a decision with incomplete information.
Give a concise overview of your hands-on experience with AWS, including the services you've used and the duration or scale of your work.
Choose one or two projects where you leveraged AWS services to solve a meaningful problem. Describe the context, your role, and the AWS services involved.
Discuss why you chose specific AWS services over alternatives, including any trade-offs in cost, performance, scalability, or complexity.
Explain how you integrated AWS services with other systems or APIs, and how you overcame any challenges that arose.
Share the measurable impact of your work (e.g., reduced latency, cost savings) and what you learned from the experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.