Full Stack Development Interview Questions: Mock Interviews

Role Skills Breakdown

Responsibilities Breakdown

Full Stack Developers design, build, ship, and maintain end-to-end features across the frontend and backend. They collaborate closely with product, design, and other engineers to translate requirements into robust, user-centric solutions. They architect data models and APIs that are scalable, secure, and maintainable. They implement responsive, accessible UIs that perform well across devices and networks. They integrate services, manage data persistence, and ensure observability and operational readiness. They write tests, review code, and contribute to standards that improve team velocity and code quality. They monitor performance, triage incidents, and continuously improve reliability. They automate build, test, and deployment pipelines to accelerate delivery and reduce risk. They document systems and communicate trade-offs to align stakeholders. Above all, they connect business goals with technical execution, ensuring customer value is delivered efficiently and safely, with a focus on quality at every layer.

• Most critical responsibilities: deliver end-to-end features across frontend and backend, design and implement scalable APIs and data models, and ensure performance, security, testing, and operational readiness.

Must-Have Skills

• JavaScript/TypeScript: Mastery of modern JS/TS enables you to write safe, maintainable code across frontend and backend. It’s fundamental for working with frameworks, tooling, and type-safe APIs.
• Frontend Frameworks (React/Vue/Angular): You should build component-based, accessible UIs with routing, state management, and performance optimizations. Understanding SSR/CSR, hydration, and code splitting is essential.
• Backend Frameworks (Node.js/Express/Nest or similar): You need to design RESTful (and sometimes GraphQL) APIs, handle middleware, and implement modular architectures. Familiarity with async patterns, streams, and error handling is key.
• Databases (SQL and NoSQL): Competency in schema design, indexing, and query optimization for relational DBs, plus modeling for document/kv stores. You must know transactions, consistency models, and when to choose each type.
• API Design & HTTP Fundamentals: You should craft resource models, status codes, idempotency, pagination, and versioning strategies. Understanding caching headers, CORS, and rate limiting is crucial.
• Security Essentials (OWASP Top 10): Protect against XSS, CSRF, SQL/NoSQL injection, SSRF, and implement secure authN/Z. You should manage secrets, validate input, and follow least privilege.
• Testing (Unit, Integration, E2E): Build test pyramids with mocking, fixtures, and reliable CI execution. Tests should be fast, deterministic, and provide clear failure signals.
• DevOps & CI/CD (Git, Docker, Cloud basics): You should automate builds, tests, and deployments; containerize services; and configure basic cloud infra. Rollbacks, blue/green, and canary strategies reduce release risk.
• Performance & Observability: Optimize bundle size, rendering, and API latency; instrument logs, metrics, and traces. Use profiling tools and set SLOs/alerts to maintain reliability.

Nice-to-Haves

• Cloud Platform Expertise (AWS/GCP/Azure): Hands-on use of managed services (RDS, S3/GCS, Cloud Run/Lambda) accelerates delivery. It’s a differentiator because you can ship reliable features faster with fewer ops burdens.
• Infrastructure as Code (Terraform/CDK/Pulumi): Codifying infra improves reproducibility and collaboration. It shows maturity in scaling systems and maintaining consistent environments.
• Advanced Observability (OpenTelemetry/Prometheus/Grafana): Deep visibility into distributed systems shortens MTTR and prevents regressions. It’s a plus because it elevates reliability and team learning.

10 Typical Interview Questions

Question 1: How would you design a scalable REST API for an e-commerce inventory service?

• Assessment Focus:
  • Ability to model resources, endpoints, and relationships clearly.
  • Consideration of scalability, consistency, and data integrity.
  • Understanding of caching, rate limiting, and versioning.
• Sample Answer:
  • I’d start by defining core resources like products, inventory items, and warehouses, and model relationships between them. Endpoints would include GET/POST/PATCH for products and inventory adjustments, with idempotent PATCH for quantity updates. To ensure consistency, I’d use transactions for critical updates and optimistic locking or version fields to prevent lost updates. I’d support pagination and filtering on listing endpoints, and expose ETags plus Cache-Control headers for read-heavy traffic. Rate limiting and API keys or OAuth scopes would protect the endpoints, while logging and tracing capture request context. I’d add API versioning via URI or header for backward compatibility. For scale, I’d separate read and write paths with replicas and consider event-driven updates to caches. I’d include monitoring of p95/p99 latency and error rates with alerts. Finally, I’d document the API with OpenAPI and provide a mock server for easy client integration.
• Common Pitfalls:
  • Ignoring concurrency issues when updating quantities in parallel.
  • Skipping versioning and cache semantics, leading to brittle clients and poor performance.
• Possible Follow-ups:
  • How would you prevent overselling during flash sales?
  • What’s your approach to API pagination and sorting at scale?
  • How would you roll out a breaking change safely?

Question 2: When would you choose SQL vs. NoSQL for a feature, and how would you model the data?

• Assessment Focus:
  • Understanding of consistency, transactions, and query patterns.
  • Ability to justify trade-offs with concrete examples.
  • Data modeling skills for both relational and document stores.
• Sample Answer:
  • I pick SQL when I need strong consistency, complex joins, and transactional guarantees, such as orders and payments. I choose NoSQL for flexible schemas, high write throughput, or large document aggregations, like product catalogs or activity feeds. In SQL, I’d normalize core entities and denormalize selectively with indexes and materialized views for performance. In NoSQL, I’d design documents around access patterns, embedding where reads are localized and referencing where data is reused. For writes under heavy load, I’d use write-optimized patterns and sharding strategies. I’d consider eventual consistency where acceptable and leverage compensating transactions for cross-collection updates. Backups, TTL policies, and migration strategies are part of my plan. I’d benchmark queries and review explain plans regularly. Ultimately, the choice is driven by SLAs, data volume, and operational maturity.
• Common Pitfalls:
  • Defaulting to one database type without analyzing access patterns.
  • Over-normalizing NoSQL or over-denormalizing SQL leading to maintenance pain.
• Possible Follow-ups:
  • How do you handle schema evolution safely in production?
  • What indexing strategy would you use for a frequently filtered list?
  • How would you model a product with variant attributes in both SQL and NoSQL?

Question 3: Describe your approach to authentication and authorization in a web application.

• Assessment Focus:
  • Knowledge of session vs. token-based auth (JWT), secure storage, and rotation.
  • Role- and attribute-based access control design.
  • Handling of multi-tenant and third-party login scenarios (OAuth2/OIDC).
• Sample Answer:
  • I start by choosing the right auth mechanism: cookies with secure, HttpOnly flags for web sessions, or short-lived JWTs with refresh tokens for APIs. I store secrets securely and rotate keys regularly, using JWKS for JWT signature verification. Authorization is role- or attribute-based with policies applied at the API gateway and service layers. I enforce least privilege and verify access on every request, including tenant isolation checks. For third-party login, I use OAuth2/OIDC flows and validate state, nonce, and PKCE where relevant. I protect against CSRF with same-site cookies or anti-CSRF tokens and ensure CORS is configured minimally. I add MFA for sensitive actions and suspicious logins. Auditing and anomaly detection help catch abuse. Finally, I document flows, build logout/rotation paths, and test failure scenarios.
• Common Pitfalls:
  • Storing long-lived tokens in localStorage or not rotating refresh tokens.
  • Implementing authorization checks only on the frontend.
• Possible Follow-ups:
  • When would you choose sessions over JWTs?
  • How do you implement tenant isolation in a multi-tenant system?
  • How do you revoke tokens and handle logout across devices?

Question 4: How do you optimize frontend performance for a large React application?

• Assessment Focus:
  • Ability to reduce bundle size and improve runtime performance.
  • Use of caching, code splitting, and rendering strategies.
  • Data fetching optimization and instrumentation.
• Sample Answer:
  • I measure with Lighthouse, WebPageTest, and RUM to set baselines and goals. I reduce bundle size via code splitting, tree shaking, and removing heavy dependencies, and I lazy load routes and components. I optimize images with modern formats, responsive sizes, and CDN delivery. I use memoization (React.memo, useMemo) prudently and avoid unnecessary renders by normalizing state. I move network-heavy and non-critical work off the critical path using prefetching and background hydration. I cache API responses with HTTP caching and client-side libraries like React Query. I ensure CSS is critical-path optimized and defer non-essential scripts. I monitor Core Web Vitals (LCP, CLS, INP) and tie alerts to regressions. Continuous profiling and budgets keep performance healthy as the app grows.
• Common Pitfalls:
  • Overusing memoization without profiling, increasing complexity and memory.
  • Ignoring image and font optimization, which often dominate load time.
• Possible Follow-ups:
  • How do you diagnose and fix a high CLS score?
  • What techniques do you use to reduce TTFB and LCP?
  • How would you set and enforce performance budgets in CI?

Question 5: Explain your state management strategy across components, pages, and network data.

• Assessment Focus:
  • Clarity on local vs. global state and server cache separation.
  • Familiarity with Redux/Context vs. data-fetching caches (React Query/SWR).
  • Ability to prevent prop drilling and over-coupling.
• Sample Answer:
  • I separate UI state (local component), global UI state (theme, auth), and server cache (remote data) clearly. Local state stays in components; global UI state uses Context or Redux when multiple consumers need it. Server state is managed with React Query/SWR to leverage caching, background refresh, and deduping. I avoid putting server data in Redux to reduce boilerplate and stale data issues. I use selectors and memoization for performance and create slice boundaries that align with features. For forms, I use libraries that support validation and async flows. I document data ownership and lifecycles to reduce coupling. I profile interaction hotspots and add lazy loading where appropriate. This approach keeps components lean and predictable as the app scales.
• Common Pitfalls:
  • Treating server data as global client state, causing staleness and complexity.
  • Excessive use of Context leading to widespread re-renders.
• Possible Follow-ups:
  • When would you pick Redux Toolkit over Context?
  • How do you handle optimistic updates and rollbacks?
  • How do you avoid waterfall requests on page load?

Question 6: Design a CI/CD pipeline for a full stack monorepo with frontend and backend services.

• Assessment Focus:
  • Pipeline stages, caching strategies, and test types.
  • Deployment strategies (blue/green/canary) and rollback plans.
  • Security scanning and environment configurations.
• Sample Answer:
  • I’d trigger on PRs with linting, type checks, unit tests, and incremental builds using caching. Then run integration tests with ephemeral environments and mocks as needed. On main, I build versioned artifacts (Docker images), run security scans (SCA/SAST), and sign images. I deploy to staging with smoke and e2e tests gated by quality checks. For production, I use blue/green or canary with automated health checks and quick rollback (version pin or traffic shift). I manage secrets via a vault and inject config at runtime. I enforce change approvals, track build provenance, and publish release notes automatically. Observability hooks verify SLOs post-deploy. This pipeline balances speed with safety and traceability.
• Common Pitfalls:
  • Skipping integration/e2e tests and relying only on unit tests.
  • Lacking a fast, deterministic rollback procedure.
• Possible Follow-ups:
  • How do you parallelize tests and optimize build times?
  • What metrics would you monitor during a canary release?
  • How do you manage environment-specific configurations?

Question 7: What is your testing strategy across unit, integration, and e2e layers?

• Assessment Focus:
  • Understanding of test pyramid and flake reduction.
  • Clear boundaries between test types and tooling choices.
  • Data seeding, fixtures, and mocking best practices.
• Sample Answer:
  • I follow a test pyramid: many fast unit tests, fewer integration tests, and a targeted set of e2e tests. Unit tests isolate logic with mocks and cover edge cases thoroughly. Integration tests validate modules working together, touching real databases or test containers to catch contract issues. E2E tests validate critical user journeys in an environment resembling production. I seed data with factories/fixtures and ensure tests are deterministic and parallelizable. I track code coverage pragmatically to find gaps, not as an absolute goal. I run smoke e2e tests in PRs and the full suite nightly. I continuously deflake tests, quarantine flaky ones, and fix root causes quickly. Test results feed into CI gates for reliable releases.
• Common Pitfalls:
  • Over-relying on e2e tests, causing slow, flaky pipelines.
  • Excessive mocking that hides integration problems.
• Possible Follow-ups:
  • How do you test time-dependent or async features reliably?
  • What’s your approach to contract testing between services?
  • How do you manage test data and teardown?

Question 8: Describe how you would handle and investigate a production incident with elevated error rates.

• Assessment Focus:
  • Structured debugging, hypothesis testing, and communication.
  • Use of logs, metrics, traces, and feature flags.
  • Rollback criteria and prevention of recurrence.
• Sample Answer:
  • I’d declare an incident, assign roles, and communicate status with impact and ETA updates. I’d examine dashboards for spikes in error codes, latency, and resource usage, then correlate with recent deploys or config changes. Using logs and distributed traces, I’d pinpoint the failing component and narrow the suspected code path. If customer impact is high, I’d roll back or disable via feature flags before deeper investigation. I’d reproduce in a staging environment with similar traffic where possible. After mitigation, I’d write a post-incident review with root cause, contributing factors, and specific action items. I’d add alerts or guardrails to catch this class of issue earlier. Finally, I’d follow up on learnings to improve runbooks and on-call health.
• Common Pitfalls:
  • Diving into code without checking dashboards and recent changes.
  • Delaying rollback when impact is clear and ongoing.
• Possible Follow-ups:
  • How do you prevent alert fatigue while staying responsive?
  • What telemetry would you add to shorten MTTR?
  • How do you design feature flags to fail safely?

Question 9: What security measures do you implement to protect a full stack application?

• Assessment Focus:
  • Knowledge of common vulnerabilities and mitigations.
  • Secure coding, secrets management, and dependency hygiene.
  • Defense-in-depth across client, server, and infrastructure.
• Sample Answer:
  • I start with input validation, output encoding, and parameterized queries to prevent injection and XSS. I enforce CSP, secure cookies, same-site policies, and anti-CSRF tokens where applicable. I restrict CORS to trusted origins and least-privilege API scopes. Secrets go into a vault with rotation and short-lived credentials; dependencies are scanned and pinned. I implement rate limiting, bot detection, and account lockouts with careful thresholds. On infra, I use network segmentation, WAFs, and hardened baselines. Logging and audit trails are tamper-evident, and alerts fire on suspicious patterns. I conduct threat modeling for new features and include security tests in CI. Regular reviews and patches keep the posture strong over time.
• Common Pitfalls:
  • Assuming CSP or a WAF alone is sufficient protection.
  • Storing secrets in code or environment files without rotation.
• Possible Follow-ups:
  • How do you defend against CSRF in a SPA with cookies?
  • What’s your strategy for secret rotation and auditing?
  • How do you securely implement file uploads?

Question 10: Outline an end-to-end caching strategy (client, edge/CDN, server, database).

• Assessment Focus:
  • Understanding of cache layers and invalidation strategies.
  • Correct use of HTTP caching headers and CDN features.
  • Data consistency and fallback logic.
• Sample Answer:
  • I’d apply HTTP caching with ETag/Last-Modified and Cache-Control directives tailored per resource. At the edge, I’d use a CDN with cache keys that include auth or vary headers where needed, plus stale-while-revalidate for resiliency. On the client, I’d employ service workers for offline support and cache static assets aggressively with content hashing. Server-side, I’d add Redis for computed responses and hot keys with TTLs and stampede protection. I’d invalidate caches on writes using events or explicit purge APIs, aiming for predictable consistency windows. I’d monitor hit ratios and latency to adjust policies. For personalized content, I’d rely on micro-caching or key segmentation to avoid leakage. I’d document invariants and failure modes, ensuring fallbacks on cache misses. This layered approach cuts latency while keeping data sufficiently fresh.
• Common Pitfalls:
  • Overly aggressive caching causing stale or incorrect personalized content.
  • Lacking coordinated invalidation, leading to subtle consistency bugs.
• Possible Follow-ups:
  • How would you cache authenticated responses safely?
  • How do you prevent cache stampedes on popular keys?
  • What metrics indicate your cache is effective?

AI Mock Interview

Recommend using an AI tool for simulated interviews—it helps you acclimate to pressure, calibrate your timing, and get immediate, targeted feedback. If I were an AI interviewer designed for this role, here’s how I would evaluate you:

Assessment One: Technical Architecture and Trade-offs

As an AI interviewer, I’ll probe your ability to design systems under constraints, asking you to sketch APIs, data models, and deployment topology for a feature. I will test whether you consider performance, reliability, security, and cost, and how you justify each trade-off. I’ll present changing requirements to see if you can adapt the design gracefully. I’ll also check if you can quantify decisions with estimates and SLOs.

Assessment Two: Practical Debugging and Operational Excellence

As an AI interviewer, I’ll simulate a production incident with logs, metrics, and traces, and ask how you’d isolate the issue. I will evaluate your hypothesis generation, use of observability tools, and criteria for rollback vs. forward-fix. I’ll assess your ability to write a concise post-incident review with actionable preventions. I’ll look for calm communication and clear decision-making under time pressure.

Assessment Three: Collaboration, Communication, and Delivery

As an AI interviewer, I’ll ask you to explain complex topics (e.g., auth flows or caching) to non-technical stakeholders. I will evaluate your clarity, structure, and ability to align timelines and scope. I’ll probe estimation, risk management, and how you negotiate trade-offs with product/design. I’ll also look for evidence of documentation habits and mentorship.

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