Treat the AI as a collaborative tool: first restate the problem and clarify requirements, then use AI to generate initial code or explore approaches, but critically review and test every suggestion. Focus on demonstrating your problem-solving process, including how you validate AI output and make trade-offs.
Pro tip: Narrate your thought process as you work, especially when you accept or reject AI suggestions, because interviewers assess your judgment and ability to work with imperfect tools. Always test edge cases and explain why you trust or modify the AI's code.
Restate the problem in your own words, ask clarifying questions about inputs, outputs, constraints, and edge cases. Confirm assumptions before writing any code.
Use AI to brainstorm approaches or generate a high-level plan, but evaluate the suggestions critically. Discuss trade-offs (time/space complexity, readability, scalability) and choose a direction.
Write code incrementally, using AI to assist with boilerplate or tricky parts. Review each AI-generated snippet for correctness, style, and edge cases before integrating.
Create test cases including edge cases, run them, and debug any failures. Explain how you verify the AI's output and fix issues.
Summarize the solution, discuss potential improvements or alternative approaches, and reflect on how AI helped or hindered. Mention any trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Four parts total and this was probably my strongest section.
Start by clarifying the integration scenarios and constraints, then outline a modular design that separates concerns (e.g., authentication, data transformation, error handling). Implement incrementally, testing each scenario, and ensure the solution is extensible for future integrations.
Pro tip: Demonstrate awareness of Stripe's API patterns (e.g., idempotency keys, webhooks, versioning) and emphasize robust error handling and observability, as these are critical in payment integrations.
Ask questions to understand the specific integration scenarios, expected scale, and any constraints (e.g., latency, data volume). Confirm the scope and prioritize scenarios.
Propose a modular design with clear separation of concerns, such as adapters for each integration, a common interface, and shared utilities for auth, retries, and logging.
Code the foundational pieces first: authentication, request/response handling, and error mapping. Then build scenario-specific logic, ensuring reusability.
Write unit and integration tests for each scenario, including edge cases like network failures and invalid data. Use mocks or sandbox environments where appropriate.
Discuss how the solution can be extended to new scenarios, and mention monitoring, logging, and documentation for maintainability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Found both bugs but my communication was a mess.
Start by clarifying the expected behavior and reproducing the bug to confirm your understanding. Then systematically narrow down the root cause using debugging tools and reasoning, and finally propose a fix while discussing trade-offs and potential side effects.
Pro tip: Narrate your thought process clearly and involve the interviewer as a collaborator—treat it like a pair debugging session. Also, always consider edge cases and write a test to prevent regression.
Ask clarifying questions to confirm the expected behavior and reproduce the bug consistently. This ensures you're solving the right problem.
Based on the symptoms and code, brainstorm potential root causes and prioritize them by likelihood. State your hypotheses aloud.
Use debugging tools (breakpoints, logging, etc.) to test your hypotheses, narrowing down the root cause step by step.
Once the root cause is identified, propose a fix, implement it, and verify that it resolves the bug without introducing new issues.
Explain the trade-offs of your fix and suggest adding tests or monitoring to prevent similar bugs in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.