Start by clarifying requirements and constraints, then outline the component architecture and data flow. Walk through the implementation details for debouncing, async fetching, keyboard navigation, and accessibility, highlighting trade-offs and edge cases. Conclude with testing and performance considerations.
Pro tip: Emphasize accessibility (ARIA roles, screen reader support) and race condition handling (e.g., using AbortController or request IDs) to show production-level thinking. Mention how you would handle caching and rate limiting, especially in a fintech context like Coinbase.
Ask about expected scale, latency requirements, API rate limits, and accessibility standards. Confirm whether suggestions should be cached and how errors should be handled.
Outline the component structure (input, suggestion list, state management) and the flow: user types -> debounce -> fetch -> update state -> render. Discuss state variables like query, suggestions, activeIndex, loading, and error.
Explain debounce implementation (e.g., using setTimeout/clearTimeout or a custom hook) and how to handle async requests with cancellation (AbortController) to prevent race conditions. Mention error handling and loading states.
Describe key event handling for ArrowUp, ArrowDown, Enter, and Escape. Explain how to manage activeIndex, highlight the active item, and submit the selected value on Enter. Ensure focus management and accessibility (ARIA roles, aria-activedescendant).
Talk about trade-offs (e.g., debounce delay vs. responsiveness, client-side vs. server-side filtering). Outline testing strategies (unit, integration, E2E) and performance optimizations (memoization, virtualization for large lists).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.