The 'placeholder function is fine' part lulled me into thinking this was easy.
Start by clarifying requirements and constraints (e.g., summarization logic, history retention, concurrency) before diving into code. Then outline the project structure with clear separation of concerns: routers, services, models, and schemas. Finally, present the code file by file, explaining key design decisions and trade-offs.
Pro tip: Emphasize how you would use an AI coding assistant effectively: by writing precise prompts, reviewing generated code critically, and iterating on tests. This shows you leverage AI as a tool while maintaining engineering rigor.
Ask questions to understand the expected summarization method (e.g., extractive, abstractive, or placeholder), history storage limits, and any authentication or rate-limiting needs. This ensures you build the right thing.
Outline the file structure and component responsibilities: routers handle HTTP, services contain business logic, models define data entities, and schemas handle validation. Mention dependency injection for testability.
Write code for each file, starting with models and schemas, then services, then routers. Use FastAPI's APIRouter, Pydantic models, and in-memory storage (e.g., a list or dict) with thread-safe access if needed.
Include input validation via Pydantic, proper HTTP status codes, and error responses. Discuss how to handle edge cases like empty input or history overflow.
Explain how you would test the endpoint (unit and integration tests) and use the AI assistant to generate test cases. Highlight any trade-offs made (e.g., in-memory vs. persistent storage).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Knew this was coming and still fumbled the first few seconds.
Start by briefly describing the system and the problem it solves, then explain the responsibilities of each layer and the trade-offs you considered. Emphasize how this separation improves maintainability, testability, and scalability, and conclude with any lessons learned or alternative approaches you evaluated.
Pro tip: Show that you understand the trade-offs by acknowledging potential downsides (e.g., added complexity) and explaining why the benefits outweigh them in your context. This demonstrates maturity and a pragmatic mindset.
Briefly describe the system, its scale, and the key requirements (e.g., performance, maintainability, team structure) that influenced your architecture.
Explain what each layer (router, service, model) does and why that separation makes sense. For example, router handles HTTP concerns, service contains business logic, model manages data access.
Discuss the trade-offs of this design (e.g., more files, indirection) and mention alternative architectures you considered (e.g., monolithic, hexagonal) and why you chose this one.
Highlight concrete benefits you observed, such as easier testing, independent scaling, or clearer ownership. Use metrics or examples if possible.
Share any challenges or things you would do differently, showing reflection and growth.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went straight to JWT middleware on the router layer.
Start by clarifying the service's architecture, data sensitivity, and client types to determine the appropriate authentication method. Then propose a solution that balances security, scalability, and user experience, and discuss trade-offs between different approaches.
Pro tip: At Meta, scale and performance are critical, so emphasize how your authentication solution handles millions of requests per second with low latency, and mention using Meta's internal auth infrastructure like OAuth or JWT with proper caching.
Ask about the service's architecture, data sensitivity, client types (web, mobile, internal), and expected traffic to tailor the authentication approach.
Select a method such as OAuth 2.0, JWT, or API keys based on requirements, and justify your choice with security and scalability considerations.
Outline how to integrate authentication into the service, including middleware, token validation, and session management, ensuring minimal performance overhead.
Discuss measures like token expiration, refresh tokens, secure storage, and protection against common attacks (e.g., CSRF, XSS).
Compare alternatives (e.g., OAuth vs. custom auth) in terms of complexity, latency, and maintainability, and explain your final recommendation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current in-memory storage and the requirements for persistence (durability, latency, scale). Then propose a layered approach: introduce a durable store (e.g., database or log) and modify the write path to persist asynchronously or synchronously based on consistency needs, while keeping reads efficient with caching. Finally, discuss trade-offs and operational concerns like schema evolution and failure handling.
Pro tip: Emphasize idempotency and backpressure: when persisting asynchronously, ensure writes are idempotent and the system can handle bursts without data loss. This shows you think about production reliability, not just adding a database.
Ask about expected write/read volume, latency tolerance, consistency requirements, and retention period. This ensures your solution aligns with actual needs.
Select an appropriate durable store (e.g., relational DB, NoSQL, append-only log) based on access patterns, scalability, and consistency needs. Justify your choice.
Integrate persistence into the request/response flow, deciding between synchronous (strong consistency) or asynchronous (higher throughput) writes. Consider batching and retries.
Add a cache (e.g., Redis) for frequently accessed history to reduce latency, and define cache invalidation strategies to keep data fresh.
Discuss schema evolution, data migration, monitoring, and failure recovery (e.g., dead-letter queues, idempotent writes) to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's architecture, critical user journeys, and failure modes to prioritize what to instrument. Then propose a layered observability strategy covering metrics, logs, and traces, and explain how each signal helps detect, diagnose, and resolve issues. Finally, discuss how you would iterate and use observability data to improve reliability.
Pro tip: Tie every instrumentation choice to a specific failure scenario or SLO, showing you understand the 'why' behind the data. Also mention the importance of high-cardinality tracing and exemplars to bridge metrics and traces for faster root cause analysis.
Ask clarifying questions about the service's architecture, dependencies, critical user paths, and SLOs. Identify what 'good' looks like and what failures would impact users most.
Map out the four golden signals (latency, traffic, errors, saturation) and any business-specific metrics. Define SLOs and error budgets to guide what to monitor and alert on.
Propose specific instrumentation: metrics for aggregate health, structured logs for detailed events, and distributed traces for request flow. Include examples of what to measure at each layer (e.g., host, service, dependency).
Describe how to set up actionable alerts based on SLOs and dashboards for real-time visibility. Emphasize reducing noise and ensuring alerts point to runbooks or automated remediation.
Explain how you would use observability data to continuously refine instrumentation, conduct postmortems, and adapt to changing service behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.