Intermediate interview questions in Python & FastAPI
How does FastAPI leverage Pydantic for request validation and serialization?
This tests your understanding of FastAPI's declarative validation. Explain that type hints trigger auto-parsing, Pydantic enforces schemas and errors, and return types auto-serialize responses. Red flag: manually parsing request.body() or json.loads in routes.
Write an async decorator that logs execution time for FastAPI
Use functools.wraps, wrap perf_counter around awaited call, log ms, and place decorator above path operation.
How do you structure concurrent API calls with asyncio.gather in FastAPI?
Tests FastAPI async concurrency. Strong answer: async def endpoint with two async HTTP requests in asyncio.gather, cutting total latency from sum to max of the two. Red flag: using sync clients or threads instead of async I/O.
How FastAPI uses type hints for validation
Hints drive parsing, validation, and conversion; a path declared int is coerced or returns 422; OpenAPI is auto-generated.
What is the difference between a path parameter and a query parameter?
Tests REST API design and FastAPI binding. A strong answer states path params identify resources in the URL while query params filter after the question mark, then codes user_id in the route and q: str | None = None in the function.
How does FastAPI distinguish required optional and default query parameters
Tests whether you know FastAPI infers query parameter optionality from Python signature defaults. Answer: no default means required, Optional[T] = None means optional, T = value sets a default, with all three in one signature.
What standard and code elements power FastAPI's auto-generated API docs?
Tests whether you know FastAPI uses the OpenAPI standard and extracts metadata from Python type hints, Pydantic models, decorators, and docstrings to build interactive docs. Red flag: claiming you must manually maintain a separate schema file.
Enforce positive price and SKU format using Pydantic Field without custom validators
Use Field(gt=0) for price and Field(pattern=r'^ITEM-\d{5}$') for SKU; mention Annotated.
Implement a custom validator for a single Pydantic model field
Use @field_validator as a classmethod, raise ValueError on failure, return the value.
How do you prevent password_hash from appearing in a FastAPI response?
Tests FastAPI response filtering and the security practice of separating DB schemas from API contracts. A strong answer proposes a dedicated output model omitting the field, then cites response_model_exclude. Red flag: manual dict deletion or monkey-patching.
Model an Order with a nested Product list in Pydantic
It tests Pydantic nested model composition. Define Product as BaseModel, then Order with products: list[Product]; Pydantic recursively coerces each dict and raises ValidationError on failure. A red flag is insisting on manual iteration.
Implement a FastAPI file upload endpoint with form data
Tests FastAPI multipart literacy. A strong answer names python-multipart, uses Annotated[UploadFile, File()] for the image, and Annotated[str, Form()] for user_id.
How do you create a reusable current-user dependency in FastAPI?
Tests DRY auth with FastAPI Depends. Answer: create get_current_user that Depends on OAuth2PasswordBearer, verifies token, returns User model, inject into routes. Red flag: middleware or manual header parsing in each endpoint.
How do you set a custom header and cookie in FastAPI?
Tests FastAPI temporal Response injection and merge behavior. Strong answer: inject Response, set headers via response.headers, cookies via set_cookie, then return the payload normally.
How would you override a FastAPI dependency during testing?
Tests your grasp of FastAPI's dependency override mechanism. A strong answer mentions app.dependency_overrides, notes that sub-dependencies are bypassed, and stresses clearing overrides after each test.
How do you use a Python class as a FastAPI dependency?
It tests whether you understand FastAPI DI beyond functions and when stateful encapsulation wins. Explain that Depends takes callable classes, centralizing setup and shared state in __init__. Red flag: claiming classes are pure syntactic sugar.
How does FastAPI execute setup and teardown in nested yield dependencies?
It tests FastAPI's dependency injection lifecycle and stack-like teardown for nested yield dependencies. Setup runs top-down; teardown runs bottom-up after the response. A red flag is claiming teardown order is arbitrary or follows garbage collection.
How does FastAPI cache dependencies within a single request?
Tests if you know FastAPI caches a dependency after the first call in a request and reuses it across the tree. A strong answer covers default use_cache=True, request-scoped lifetime, and disabling it.
How to apply a dependency to only one FastAPI router?
Pass dependencies=[Depends(auth)] to APIRouter for /users, omit it for /items, include both.
Manage dev, staging, and prod configs in a large FastAPI app
This tests separating environment config from code with pydantic-settings and env vars. A strong answer covers .env files for local dev, per-environment validation, and secret injection for prod. Red flags are hardcoded values or scattered conditionals.
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