Fastapi Interview Questions & Answers for 2026

FastAPI is built on Starlette and Pydantic. Key advantages: automatic OpenAPI/Swagger documentation generated from type hints, native async/await support for high-concurrency I/O-bound workloads, Pydantic models for automatic request validation and serialisation, dependency injection system for clean code organisation, and significantly higher throughput compared to Flask in benchmarks. Django REST Framework is batteries-included and better for complex applications needing an ORM and admin interface. FastAPI is better for high-performance microservices and APIs where you want minimal overhead and modern Python typing.

FastAPI uses Pydantic models as type annotations for request bodies. Define a class inheriting from pydantic.BaseModel with typed fields. FastAPI automatically validates incoming JSON against the model, converts types, and returns a 422 Unprocessable Entity response with detailed error messages if validation fails — with no manual validation code. You can add validators using @field_validator or Pydantic's Field() for constraints like min/max length, regex, and ranges. This eliminates an entire class of manual validation boilerplate and provides automatic API documentation of request schemas.

FastAPI's Depends() function allows declaring reusable dependencies that are automatically resolved and injected into route handlers. Common uses: database session management (open session, yield, close in finally), authentication (extract and validate JWT token), query parameter parsing, and permission checks. Example: async def get_db(): session = Session(); try: yield session; finally: session.close(). Then in a route: def get_users(db = Depends(get_db)). Dependencies can depend on other dependencies, creating a tree. FastAPI resolves and caches them per request.

Install python-jose and passlib. Create a route POST /token that accepts form credentials, verifies the password against the hashed version in the database, and returns a signed JWT token. Create a get_current_user dependency that reads the Authorization header, decodes the JWT using the secret key, extracts the user ID, and fetches the user. Use Depends(get_current_user) on protected routes. For token expiry include an exp claim in the JWT payload. Use OAuth2PasswordBearer as the security scheme for automatic Swagger UI authentication support.

FastAPI provides BackgroundTasks for running functions after returning the HTTP response — useful for sending emails, logging, or cache updates that should not block the response. Inject BackgroundTasks into the route and call background_tasks.add_task(func, arg1, arg2). The function runs after the response is sent. For heavier background work use Celery with Redis or RabbitMQ as the broker, or ARQ (async Redis Queue) which integrates better with async FastAPI. BackgroundTasks are suitable for lightweight fire-and-forget tasks; task queues are better for retries, scheduling, and distributed workers.

Path parameters are defined in the route path and are required: @app.get("/users/{user_id}"). Query parameters are function parameters not in the path: def get_users(skip: int = 0, limit: int = 10) — they appear in the URL as /users?skip=0&limit=10. Request body parameters are Pydantic model arguments — FastAPI reads JSON from the request body. FastAPI automatically determines type from the function signature: singular types become query params, Pydantic models become body params. You can mix all three in one route. Path and query params are validated and converted automatically.

Alembic is SQLAlchemy's migration tool. Set up with alembic init alembic in your project root. Configure alembic.ini and env.py to point at your SQLAlchemy metadata. Generate a migration with alembic revision --autogenerate -m "add users table" — Alembic compares your models to the database schema and writes the migration file. Apply with alembic upgrade head, roll back with alembic downgrade -1. In production run migrations as part of the deployment step before the app starts. For async databases like databases or asyncpg use a synchronous connection in env.py for migration execution.

Organise by feature, not by type. Create a routers/ directory with one file per resource (users.py, products.py). Each router file defines an APIRouter with its prefix and tags. Include all routers in main.py with app.include_router(). Put Pydantic schemas in schemas/, database models in models/, business logic in services/, and shared dependencies (auth, db session) in dependencies.py. Use lifespan context manager (asynccontextmanager) for startup/shutdown events (opening DB connections, loading ML models). This structure keeps related code together and makes each feature independently testable.