This is where I spent most of my time and still felt like I was leaving things out.
Start by clarifying the requirements and demo to ensure alignment, then walk through the architecture layer by layer, justifying each decision with trade-offs. Emphasize how the choices support scalability, maintainability, and user experience, and tie them back to the specific needs of the product.
Pro tip: Always connect technical decisions to business impact and user experience—this shows you think like a product engineer, not just a coder. Also, proactively mention trade-offs you considered, demonstrating maturity in decision-making.
Ask questions to understand the functional and non-functional requirements, target users, and constraints from the demo. This ensures your architecture addresses the actual needs.
Outline the overall structure: framework choice (e.g., React, Vue, Angular), routing strategy, and how components will be organized. Briefly explain why these choices fit the requirements.
Break down the component hierarchy, describe state management (local vs global, libraries like Redux, Zustand, or Context), and how data flows between components.
Explain your data fetching approach (REST, GraphQL, React Query, etc.), how you handle loading and error states, and how you ensure accessibility (ARIA, keyboard navigation, semantic HTML).
Discuss performance techniques (code splitting, lazy loading, memoization, image optimization) and your testing approach (unit, integration, E2E, accessibility tests).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The mapping part is what makes this harder than a typical API design question.
Start by clarifying the application's core use cases and data model, then define RESTful endpoints with clear resource naming and HTTP semantics. Specify request/response schemas using JSON examples, and explicitly cover pagination, authentication, and error handling. Finally, map each UI state (loading, empty, error, success) to specific API calls and response handling.
Pro tip: Emphasize idempotency and versioning in your API design—this shows foresight about production concerns and backward compatibility. Also, mention how you'd document the contract (e.g., OpenAPI) to keep frontend and backend in sync.
Identify the main entities (e.g., users, items) and their relationships. Confirm the operations needed (CRUD, search, etc.) to define the API's surface area.
List each endpoint with its HTTP method, path, and purpose. Use RESTful conventions: nouns for resources, plural names, and proper status codes.
For each endpoint, provide JSON examples of request bodies and response payloads. Include field types, required/optional flags, and error response formats.
Explain pagination strategy (e.g., cursor-based or offset/limit) with query parameters and response metadata. Describe auth mechanism (e.g., JWT, OAuth) and how tokens are passed and validated.
For each UI state (initial load, loading, empty, error, success), specify which API call(s) are made, what parameters are sent, and how the response is handled to render the state.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a second interview crammed into the same session.
Start by clarifying the application's core requirements and scale (e.g., read/write ratio, latency, consistency needs). Then propose a high-level architecture with clear service boundaries and data models, and dive into each required area (storage, caching, background jobs, idempotency, observability, scaling) with concrete choices and trade-offs. Finally, discuss how the system evolves as load increases.
Pro tip: Always tie your design decisions back to the specific application requirements and constraints—interviewers value justified trade-offs over buzzwords. Also, proactively mention how you'd measure success (e.g., SLOs) and handle failures, as this shows production maturity.
Ask questions to understand the application's core features, expected traffic (QPS, data volume), latency and consistency requirements, and any compliance needs. This ensures your design is grounded in reality.
Identify the main domains and propose microservices or modular monolith boundaries. Sketch key entities, relationships, and access patterns to inform storage choices.
Select appropriate databases (SQL/NoSQL) per service based on consistency and query needs. Describe caching layers (e.g., Redis) and invalidation strategies to meet latency goals.
Explain how you'll ensure idempotent operations (e.g., idempotency keys), handle asynchronous work with queues, and instrument the system with logging, metrics, and tracing.
Discuss horizontal scaling, partitioning/sharding, read replicas, and how the architecture can evolve (e.g., from monolith to microservices) as load grows.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short section in the interview, maybe five minutes.
Structure your answer around a concrete, end-to-end deployment story: start with the CI pipeline, then describe environment configuration and promotion, and finish with canary releases and rollback mechanisms. Emphasize trade-offs and how you'd measure success at each stage, tying decisions back to reliability and rapid iteration.
Pro tip: Anchor your answer in a specific past deployment you owned, and quantify outcomes (e.g., 'reduced rollback time from 30 to 5 minutes'). This shows you've operated real systems, not just read about them.
Describe how code changes trigger automated builds, unit/integration tests, and produce immutable artifacts (e.g., Docker images) tagged with commit SHA. Mention gating on test coverage and security scans.
Explain how you manage config across dev/staging/prod using environment variables, secrets managers, and infrastructure-as-code. Describe promotion gates (e.g., manual approval for prod) and parity between environments.
Outline a canary rollout: deploy to a small subset of traffic (e.g., 1-5%), monitor key metrics (latency, error rate, business KPIs), and gradually increase traffic if healthy. Mention automated analysis and rollback triggers.
Detail your rollback plan: automated rollback on metric degradation, blue-green or versioned deployments for instant revert, and database migration strategies (backward-compatible changes). Include post-mortem and learning loop.
Highlight monitoring, logging, and tracing to detect issues early. Discuss how you iterate on the pipeline based on incidents and deployment metrics (e.g., DORA metrics).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Choose a concrete, high-impact user flow (e.g., login, checkout, or search) and narrate it step-by-step, explicitly linking each UI action to its API call, including request/response details and error handling. Then, describe how you would validate the design against a demo, prioritize features under time constraints, and resolve ambiguities through stakeholder alignment and iterative clarification.
Pro tip: Show that you think in terms of trade-offs and user impact: when scoping under time pressure, explicitly state what you would cut and why, and how you would communicate that to stakeholders. This demonstrates product sense and cross-functional maturity.
Pick a critical user flow relevant to the role (e.g., user authentication, payment, or data retrieval) and briefly state its importance. Outline the high-level steps from user entry to completion.
Walk through each UI action in sequence, specifying the corresponding API call (method, endpoint, payload, response). Include error states and edge cases, and explain how the UI updates based on API responses.
Describe how you would compare your design to the provided demo: identify key interactions, data flows, and visual states. Propose a validation plan (e.g., side-by-side walkthrough, user testing, or automated checks) to ensure alignment.
Explain how you would prioritize features using a framework like MoSCoW or impact/effort matrix. Identify must-haves vs. nice-to-haves, and describe how you would communicate trade-offs and adjust scope with stakeholders.
Outline your approach to ambiguity: ask clarifying questions, propose assumptions, and iterate with stakeholders. Emphasize documenting decisions and validating assumptions early through prototypes or spikes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.