This was the part I actually felt good about.
Start by clarifying the problem scope and requirements, then propose a high-level architecture that addresses scalability, reliability, and performance. Deep dive into messaging and caching components, explaining trade-offs and how they integrate with the overall system.
Pro tip: Tie your design decisions back to Walmart's scale and business needs, such as handling peak traffic during Black Friday, to show you understand real-world constraints.
Ask questions to understand functional and non-functional requirements, such as expected user load, data volume, latency, consistency, and availability needs.
Sketch the main components (e.g., load balancers, application servers, databases) and how they interact, focusing on scalability and fault tolerance.
Choose a messaging system (e.g., Kafka, RabbitMQ) based on requirements, and explain how it enables asynchronous communication, decoupling, and load leveling.
Select a caching strategy (e.g., Redis, Memcached) and discuss cache invalidation, eviction policies, and how caching improves performance and reduces database load.
Discuss trade-offs (e.g., consistency vs. availability, cost vs. performance) and how the system scales horizontally, including sharding, replication, and partitioning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and constraints with the interviewer, then outline your approach before coding. Implement a working solution first, then optimize if time allows, and test with edge cases.
Pro tip: Communicate your thought process continuously and treat the interviewer as a collaborator—ask for hints if stuck, but show you can debug independently.
Restate the problem in your own words and ask clarifying questions about input/output, constraints, and edge cases. Confirm assumptions before proceeding.
Discuss possible algorithms or data structures, compare trade-offs, and choose the most efficient one that fits the constraints. Outline the steps in pseudocode.
Write clean, modular code while explaining your reasoning. Use meaningful variable names and handle edge cases as you go.
Walk through your code with a sample input, including edge cases. If you find a bug, debug it methodically and explain your fix.
If time permits, discuss potential optimizations and analyze time/space complexity. Mention any trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The feedback called out gaps in requirement gathering specifically, which stings because I know I skipped clarifying questions in the coding round.
Demonstrate a structured approach to requirement gathering by first restating the problem in your own words and asking targeted questions to uncover constraints, edge cases, and success criteria. Then, propose a high-level solution and iteratively refine it based on feedback, ensuring alignment with both technical and business goals.
Pro tip: Show that you prioritize understanding the 'why' behind requirements before diving into implementation, and proactively identify potential ambiguities or missing information that could impact the solution.
Paraphrase the problem to confirm understanding and ask open-ended questions to uncover implicit requirements, constraints, and assumptions.
Determine who the solution is for and what business or user goals it should achieve, ensuring alignment with Walmart's scale and customer focus.
Probe for edge cases, performance requirements, data volume, and integration points that could affect the design.
Outline a high-level approach, then validate it with the interviewer by discussing trade-offs and seeking feedback.
Refine the solution based on feedback, and summarize the agreed-upon requirements and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.