Pretty easy, nothing that would stress you out.
Clarify the problem requirements and constraints, then outline your approach before coding. Write clean, correct code with meaningful names, and test with edge cases while explaining your reasoning.
Pro tip: Verbalize your thought process and trade-offs as you code; interviewers value clear communication and problem-solving over silent perfection.
Ask questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss possible solutions, choose the most efficient one, and outline steps or pseudocode before writing actual code.
Write clean, readable Java code with proper syntax, meaningful variable names, and modular structure.
Walk through your code with sample inputs, including edge cases like empty input, null, or large values. Fix any issues.
State time and space complexity, and mention potential improvements or alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the round that felt most like a real technical bar.
Start by clarifying the problem constraints and edge cases, then discuss potential approaches (e.g., brute force, optimized) with time/space complexity before coding. Write clean, modular Java code with meaningful variable names, and test with examples including edge cases.
Pro tip: Verbalize your thought process continuously, especially when stuck, and proactively discuss trade-offs between different solutions to demonstrate depth. Walmart values practical, scalable solutions, so relate your approach to real-world scenarios if possible.
Restate the problem in your own words, ask clarifying questions about input size, constraints, and edge cases, and confirm expected output.
Discuss multiple strategies, from brute force to optimized, and analyze their time and space complexities. Choose the best one based on constraints.
Outline the algorithm step-by-step, then implement it in Java with clean, readable code. Use appropriate data structures and handle edge cases.
Walk through your code with a few test cases, including edge cases, and fix any bugs. Explain how you would test for correctness and performance.
If time permits, suggest further optimizations or alternative solutions, and discuss potential trade-offs and real-world applications.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the scope and requirements of the microservice architecture, then discuss key principles like bounded contexts, decentralization, and resilience. Use real-world examples from your experience to illustrate trade-offs, and emphasize how you would handle challenges such as data consistency and inter-service communication.
Pro tip: Demonstrate maturity by acknowledging that microservices are not a silver bullet; discuss when a monolith might be more appropriate and how to evolve systems incrementally. Also, highlight the importance of organizational alignment (Conway's Law) and DevOps culture for successful microservices adoption.
Ask questions to understand the system's scale, business domain, and non-functional requirements (e.g., latency, consistency, team structure). This ensures your answer is tailored to the context.
Discuss fundamental microservice principles such as single responsibility, bounded contexts, decentralized data management, and design for failure. Explain how these guide architectural decisions.
Talk about common challenges like service discovery, inter-service communication (sync vs. async), data consistency (Sagas, event sourcing), and operational complexity. Compare trade-offs with alternatives like monoliths or serverless.
Sketch a sample architecture for a relevant domain (e.g., e-commerce order processing), identifying services, communication patterns, and data stores. Keep it abstract but concrete enough to show understanding.
Explain how to evolve from a monolith, adopt DevOps practices (CI/CD, observability), and ensure organizational alignment. Mention anti-patterns to avoid, such as distributed monoliths.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They gave me some code to review and critique.
Treat the code review as a collaborative conversation, not a fault-finding mission. Start by understanding the code's purpose and context, then systematically evaluate correctness, readability, and maintainability, offering specific, actionable feedback. Balance technical depth with interpersonal skills, showing you can both give and receive critique constructively.
Pro tip: Before diving into details, ask clarifying questions about the code's requirements and constraints—this shows you think about the bigger picture and avoids nitpicking on irrelevant issues. Also, explicitly separate must-fix issues from optional suggestions to demonstrate prioritization.
Ask about the code's goal, constraints, and expected behavior. Quickly scan for overall structure and identify the author's intent before critiquing.
Check for logical errors, off-by-one mistakes, unhandled inputs, and potential race conditions. Verify that the code meets its requirements.
Look at naming, function length, comments, and modularity. Suggest improvements that make the code easier to understand and modify.
Identify bottlenecks, inefficient algorithms, or unnecessary resource usage. Discuss trade-offs between performance and simplicity.
Summarize findings, separating critical issues from minor suggestions. Offer concrete alternatives and explain the reasoning behind each point.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.