Start by clarifying the problem constraints and edge cases, then discuss a brute-force solution before optimizing with an efficient data structure or algorithm. Walk through the time and space complexity trade-offs, and finally implement and test the solution with examples.
Pro tip: Amazon values customer obsession and ownership, so explicitly connect your solution to scalability and real-world impact, such as handling large inputs efficiently. Also, proactively test your code with edge cases to demonstrate thoroughness.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then propose optimizations using appropriate data structures or algorithms. Compare trade-offs in time and space complexity.
Outline your chosen approach step-by-step, then write clean, modular code. Use meaningful variable names and handle edge cases explicitly.
Walk through your code with normal and edge-case inputs, including empty inputs, large values, and duplicates. Debug and fix any issues.
State the final time and space complexity, and discuss potential further optimizations or alternative solutions if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints of the AI backend, such as expected load, latency, and data characteristics. Then propose a high-level architecture, diving into key components like data ingestion, model serving, and scaling. Discuss trade-offs between different design choices, emphasizing Amazon's leadership principles like customer obsession and bias for action.
Pro tip: Quantify your decisions with rough calculations (e.g., QPS, storage needs) to demonstrate practical engineering judgment. Also, relate your design to AWS services (e.g., SageMaker, Lambda, DynamoDB) to show familiarity with Amazon's ecosystem.
Ask questions to understand functional and non-functional requirements: what the AI model does, expected traffic, latency, data volume, and consistency needs.
Sketch the main components: data pipeline, model training/serving, API layer, and storage. Explain how they interact.
Elaborate on critical parts: model deployment (real-time vs batch), scaling (horizontal/vertical), and handling failures.
Discuss pros and cons of your choices, such as using serverless vs containers, and how you'd optimize for cost, performance, and scalability.
Summarize the design, mention monitoring and iteration, and tie back to business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.