Master Full Stack Developer Interviews
Real-world questions, expert answers, and a practice pack to help you shine in every interview round.
- Cover technical, system‑design, and behavioral topics
- Provide STAR‑structured model answers
- Include follow‑up probes and evaluation criteria
- Offer a timed practice pack for realistic rehearsal
Core Technical
While building a single‑page app, the team needed to fetch data from an API without freezing the UI.
Choose the appropriate execution model for the data request and subsequent UI update.
Described that synchronous code blocks the call stack until completion, causing UI lag. Explained that asynchronous code—using callbacks, promises, or async/await—allows the call stack to continue while the request is pending. Gave a concrete example: using fetch with async/await to load user data after the page renders, versus a synchronous loop that would block rendering.
The UI remained responsive, data loaded seamlessly, and the team reduced perceived load time by 30%.
- How do promises improve error handling compared to callbacks?
- When might you still prefer a synchronous approach?
- Clarity of concepts
- Correct use of terminology (event loop, call stack)
- Relevant example
- Understanding of performance impact
- Vague description without distinguishing blocking vs non‑blocking
- No concrete example
- Define synchronous execution (blocking)
- Define asynchronous execution (non‑blocking)
- Explain event loop role
- Provide example with fetch/async‑await
- Contrast with a blocking loop
Tasked with creating a public API for a new blogging SaaS product.
Define resources, HTTP methods, authentication, and versioning to ensure scalability and security.
Identified core resources: users, posts, comments, tags. Mapped CRUD operations to endpoints (e.g., GET /api/v1/posts, POST /api/v1/posts). Chose JWT for stateless auth, implemented pagination, rate limiting, and HATEOAS links. Discussed data validation, error handling, and API documentation with OpenAPI.
The API supported 10k daily requests with <2% error rate and enabled third‑party integrations within the first month.
- How would you handle media uploads for blog posts?
- What strategies would you use for API version deprecation?
- Comprehensiveness of endpoints
- Security considerations
- Scalability measures
- Clarity of design rationale
- Missing authentication plan
- Overly generic endpoints without resource hierarchy
- List main resources (users, posts, comments, tags)
- Define endpoint patterns with HTTP verbs
- Explain authentication (JWT) and versioning
- Address pagination, filtering, rate limiting
- Mention error handling and documentation
System Design
Needed to build a chat service supporting thousands of concurrent users with low latency.
Select a tech stack that enables real‑time messaging, horizontal scaling, and reliable persistence.
Chose React with Redux for the frontend UI, WebSocket (Socket.io) for bi‑directional communication, Node.js with NestJS for the backend API, and Redis Pub/Sub for message broadcasting. Used PostgreSQL for durable storage of chat history and Elasticsearch for search. Deployed on Kubernetes with auto‑scaling, used Docker containers, and set up CI/CD pipelines with GitHub Actions and Helm charts. Implemented health checks and monitoring via Prometheus/Grafana.
The system handled 50k concurrent connections with sub‑100 ms message latency and zero downtime during deployments.
- How would you ensure message ordering across multiple instances?
- What fallback mechanism would you implement if WebSocket fails?
- Appropriate tech choices for real‑time
- Scalability strategy
- Data consistency handling
- Ops and deployment plan
- Suggesting only polling instead of WebSocket
- Ignoring horizontal scaling
- Frontend: React + Redux for UI state
- WebSocket (Socket.io) for real‑time channel
- Backend: Node.js/NestJS handling sockets and REST
- Redis Pub/Sub for message distribution
- PostgreSQL for persistence, Elasticsearch for search
- Kubernetes + Docker for scaling
- CI/CD with GitHub Actions + Helm
The team maintained 8 microservices (frontend, API gateway, auth, payments, etc.) with frequent releases.
Create an automated pipeline that builds, tests, and deploys each service independently while ensuring integration integrity.
Set up a monorepo with Nx for shared tooling. Used GitHub Actions to trigger pipelines on PR merge. Each service runs unit tests, integration tests in Docker containers, and static code analysis. Built Docker images and pushed to a private registry. Deployed to Kubernetes via Helm charts with canary releases and automated health checks. Integrated Slack notifications and automated rollback on failed health probes.
Reduced release cycle from bi‑weekly to daily, with 95% of deployments succeeding without manual intervention.
- How would you handle database schema migrations across services?
- What security scans would you embed in the pipeline?
- Pipeline completeness
- Isolation of services
- Rollback strategy
- Monitoring/notification
- Single pipeline for all services causing bottlenecks
- No testing stage
- Monorepo with shared tooling (Nx)
- GitHub Actions per service
- Run unit & integration tests in containers
- Build Docker image, push to registry
- Deploy with Helm to Kubernetes
- Canary releases + health checks
- Rollback automation
- Notification integration
Behavioral
Inherited a Node.js monolith with tangled business logic and no test coverage, causing frequent bugs.
Improve maintainability and enable future feature development without breaking existing functionality.
First added unit tests for critical paths using Jest. Applied the Strangler Fig pattern: extracted modules into separate services (auth, notifications) behind an API gateway. Refactored code to use TypeScript for type safety, introduced ESLint and Prettier, and set up CI to enforce standards. Conducted code reviews and pair programming sessions to spread knowledge.
Bug rate dropped by 60%, deployment frequency increased from monthly to weekly, and the team could add new features with confidence.
- How did you prioritize which parts to refactor first?
- What challenges did you face with the lack of documentation?
- Structured approach
- Emphasis on testing
- Clear outcome metrics
- Collaboration
- No mention of testing or metrics
- Add tests to create safety net
- Identify modular boundaries
- Extract services using Strangler Fig
- Migrate to TypeScript
- Enforce linting and CI
- Team collaboration
The product team needed a responsive dashboard for real‑time analytics to launch at a major conference in two weeks.
Deliver the UI, integrate with existing APIs, and ensure cross‑browser compatibility within the deadline.
Held a kickoff sprint planning session with designers and PMs to define MVP scope. Used Figma prototypes to extract design tokens, built reusable React components with Styled‑Components, and set up Storybook for rapid UI validation. Implemented feature flags to toggle incomplete sections. Conducted daily stand‑ups to surface blockers and re‑prioritized tasks. Leveraged the existing CI pipeline for quick feedback.
The dashboard was live on schedule, received positive feedback for performance and visual fidelity, and increased demo sign‑ups by 25% at the conference.
- How did you handle conflicting feedback from designers and PMs?
- What testing strategy did you use to ensure cross‑browser support?
- Collaboration and communication
- Technical execution under pressure
- Outcome relevance
- Blaming others for deadline issues
- Kickoff meeting to align scope
- Extract design tokens from Figma
- Build reusable React components
- Use Storybook for UI QA
- Feature flags for incremental delivery
- Daily stand‑ups for coordination
- JavaScript
- Node.js
- React
- REST APIs
- Docker
- CI/CD
- SQL
- NoSQL
- Microservices
- TypeScript
