FastAPI Interview Questions for 2 Years Experience

FastAPI is built on Starlette (ASGI) and uses async/await natively, allowing it to handle thousands of concurrent requests without blocking. Unlike Flask (WSGI, synchronous), FastAPI can call OpenAI, Pinecone, and PostgreSQL concurrently in a single request. Benchmarks show FastAPI handling 3–10x more requests per second than Flask for I/O-bound AI workloads.

Use FastAPI's Depends() system. Create a get_db() function that yields a database session, then inject it: async def endpoint(db: Session = Depends(get_db)). For AI apps, use this pattern for LLM clients, vector stores, and connection pools — ensures resources are properly initialized and cleaned up per request, and makes testing easy by overriding dependencies in tests.

Pydantic V2 rewrites the core in Rust, making validation 5–50x faster. Key changes: model_config instead of class Config, @model_validator replaces @validator, field_validator for field-level validation, and model_dump() replaces dict(). For FastAPI, this means faster request validation and serialization — critical for high-traffic AI APIs processing thousands of requests per minute.

Middleware intercepts every request and response. Add with app.add_middleware(). In AI APIs, use middleware for: (1) Request ID injection for tracing LLM calls, (2) Latency logging to identify slow embeddings, (3) API key authentication, (4) Cost tracking by counting OpenAI tokens per request, (5) CORS for frontend AI tools. Keep middleware lightweight — heavy processing should happen in background tasks.

Use TestClient (sync) or AsyncClient from httpx (async). Mock external AI calls with unittest.mock.AsyncMock or pytest-mock. Pattern: override the get_llm_client dependency in app.dependency_overrides, return a mock that returns predictable responses. Test: (1) happy path, (2) OpenAI timeout/error handling, (3) rate limiting, (4) input validation. Never call real APIs in unit tests — use fixtures with example responses.

response_model tells FastAPI to validate and serialize the response through a Pydantic model — it filters out extra fields and ensures type safety. model_validate is a Pydantic method to create a model instance from a dict. In AI APIs, use response_model to strip internal data (embeddings, raw LLM outputs) before returning to clients. This also generates correct OpenAPI documentation.