I decided to do both options instead of one.
Start by thoroughly understanding the codebase and the two change options, then choose the one that best balances impact, risk, and your ability to implement it cleanly. Implement the change incrementally with tests, and be prepared to explain your trade-off analysis and debugging process.
Pro tip: Before writing any code, run the existing tests to establish a baseline and identify any flaky tests; this saves time later and shows you prioritize reliability.
Explore the project structure, run the test suite, and read relevant code to grasp the architecture and the two change options. Identify dependencies and potential impact areas.
Compare the two options based on complexity, risk, test coverage, and alignment with project goals. Document your reasoning to articulate later.
Make small, testable changes, running tests frequently to catch regressions early. Write new tests if needed to cover the change.
If tests fail, use root cause analysis to identify the issue, fix it, and ensure all tests pass. Refactor if necessary for clarity.
Review your changes for quality, and prepare to explain your approach, trade-offs, and any challenges faced during the process.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I got maybe 45 minutes for something scoped for a full hour, still rattled from the debugging session, autocomplete not working, not the fastest typist.
Start by clarifying requirements and scoping the MVP to a single resource (e.g., reservations) with CRUD operations, then outline a minimal tech stack and testing strategy. Walk through the design and implementation plan step-by-step, emphasizing trade-offs and how you would validate with tests within the time limit.
Pro tip: Demonstrate prioritization by explicitly stating what you will not build (e.g., authentication, complex business rules) and why, showing you can deliver a working, tested API under time pressure.
Ask clarifying questions to define the API's core functionality, data model, and constraints. Agree on a minimal viable product (MVP) that can be built and tested within one hour.
Sketch the REST endpoints (e.g., GET /reservations, POST /reservations) and the reservation schema. Choose a simple stack (e.g., Python/Flask, Node/Express) and an in-memory or lightweight database.
Code the CRUD operations for reservations, focusing on happy paths first. Use a modular structure to keep code clean and testable.
Write unit and integration tests for each endpoint, covering success and error cases. Run tests to ensure they pass and catch regressions.
Summarize what was built, what was skipped, and potential improvements. Discuss how you would extend the API for production (e.g., auth, persistence, validation).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the one part where I felt like myself again.
Start by clarifying the problem scope and requirements with the interviewer, then outline a high-level design before diving into details. Focus on scalability, reliability, and trade-offs, and be prepared to iterate based on feedback.
Pro tip: Demonstrate awareness of Samsung's product ecosystem (e.g., SmartThings, Galaxy devices) by relating your design to real-world constraints like low-power devices or cross-device synchronization. Always discuss monitoring, deployment, and failure recovery to show production maturity.
Ask questions to understand functional and non-functional requirements, such as expected scale, latency, consistency, and budget. Confirm the scope and constraints before proceeding.
Sketch the main components (e.g., clients, servers, databases, caches) and their interactions. Explain how data flows through the system and how it meets the core requirements.
Choose critical components and detail their design, including data models, APIs, and algorithms. Discuss trade-offs between different approaches (e.g., SQL vs NoSQL, push vs pull).
Explain how the system scales horizontally, handles failures, and ensures data consistency. Mention techniques like sharding, replication, load balancing, and circuit breakers.
Summarize the design, reiterate key decisions, and discuss potential bottlenecks or future improvements. Be open to interviewer feedback and adapt your design accordingly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.