Top 30 Python & FastAPI Interview Questions and Answers

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1. Question 1 of 30

   When you annotate a FastAPI route parameter with a Pydantic model, what does the framework do with that type hint?

   • aIt leverages the hint for automatic request validation and OpenAPI schema generation
   • bIt uses the hint to enforce types at runtime via Python's built-in type checker
   • cIt passes the hint to the IDE but performs no framework-level request handling
   • dIt ignores the hint and expects manual validation logic inside the route function
   Show the answer

   Answer: a · It leverages the hint for automatic request validation and OpenAPI schema generation

   FastAPI reads type hints at startup to construct Pydantic models that validate incoming requests and generate OpenAPI schemas automatically. The distractor about runtime enforcement is wrong because Python itself ignores type hints during execution unless an external tool checks them.

   Read the full bite: Explain Python type hints and their importance in FastAPI

2. Question 2 of 30

   You write an async def FastAPI endpoint that calls requests.get. What is the main risk?

   • aFastAPI automatically moves the blocking call into a threadpool so it remains safe.
   • bIt runs correctly but is only slightly slower than using def for the same blocking call.
   • cThe event loop is blocked, freezing concurrent request handling until the call finishes.
   • dPython raises a SyntaxError because blocking libraries cannot be used inside async def.
   Show the answer

   Answer: c · The event loop is blocked, freezing concurrent request handling until the call finishes.

   The correct answer is C because calling a blocking library like requests inside async def stalls the event loop, stopping all other requests. The most tempting distractor is A because beginners often assume FastAPI magically threadpools any blocking code, but only def endpoints are run in a threadpool.

   Read the full bite: What is the difference between def and async def in Python and FastAPI?

3. Question 3 of 30

   What mechanism triggers FastAPI to automatically validate and parse an incoming JSON request body against a schema?

   • aSetting the response_model argument on the route decorator
   • bUsing a Pydantic model as the type hint for a route parameter
   • cAdding an explicit validate decorator to the route handler
   • dManually calling json.loads on request.body inside the route function
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   Answer: b · Using a Pydantic model as the type hint for a route parameter

   FastAPI inspects function signature type hints at runtime, so using a Pydantic model as a parameter type hint automatically triggers request parsing and validation. Manually calling json.loads inside the route is a red flag that ignores this declarative mechanism, and response_model governs response serialization, not request validation.

   Read the full bite: How does FastAPI leverage Pydantic for request validation and serialization?

4. Question 4 of 30

   When building an async execution-time decorator for FastAPI, why is omitting functools.wraps on the inner wrapper considered a red flag?

   • aIt causes the wrapper to run synchronously, blocking the event loop during high-concurrency requests.
   • bIt strips the original function's metadata, which breaks FastAPI's OpenAPI schema generation and dependency injection.
   • cIt makes FastAPI treat the endpoint as synchronous, offloading it to a thread pool and skewing the timer.
   • dIt prevents the decorator from awaiting the original coroutine, leading to unhandled runtime warnings.
   Show the answer

   Answer: b · It strips the original function's metadata, which breaks FastAPI's OpenAPI schema generation and dependency injection.

   functools.wraps preserves the original function's name, signature, and metadata, which FastAPI relies on to generate OpenAPI docs and resolve dependencies; omitting it exposes the wrapper's metadata instead. Distractor A is wrong because wraps has no effect on whether code runs synchronously or blocks the event loop.

   Read the full bite: Write an async decorator that logs execution time for FastAPI

5. Question 5 of 30

   Which implementation correctly minimizes total latency when a FastAPI endpoint must fetch data from two independent external APIs?

   • aInside an async def endpoint, pass two httpx.AsyncClient coroutines to asyncio.gather and await the result.
   • bInside an async def endpoint, pass two synchronous requests.get calls to asyncio.gather and await the result.
   • cInside an async def endpoint, await an httpx.AsyncClient call to the first API, then await a second call to the other API.
   • dInside an async def endpoint, run two synchronous requests.get calls concurrently using threading.Thread.
   Show the answer

   Answer: a · Inside an async def endpoint, pass two httpx.AsyncClient coroutines to asyncio.gather and await the result.

   asyncio.gather with an async HTTP client schedules both I/O-bound coroutines concurrently on the event loop, reducing total latency to roughly the slower call. Option C is tempting because it uses async/await correctly, but sequential awaiting means the second request cannot start until the first finishes, so latencies add up.

   Read the full bite: How do you structure concurrent API calls with asyncio.gather in FastAPI?

6. Question 6 of 30

   When a FastAPI endpoint awaiting asyncpg is suspended during a database query, how can Uvicorn process another incoming connection in the same worker process?

   • aThe GIL is released during the network wait, allowing a second Python thread in the same process to execute the new connection's handler.
   • basyncio transparently migrates the suspended coroutine to a background thread so the main event loop remains free.
   • cUvicorn spawns a lightweight thread for each connection, and the OS scheduler interleaves them while the database coroutine blocks.
   • dThe event loop yields the coroutine at the await, registers the socket with epoll or kqueue, and schedules the new connection's coroutine on the same thread.
   Show the answer

   Answer: d · The event loop yields the coroutine at the await, registers the socket with epoll or kqueue, and schedules the new connection's coroutine on the same thread.

   The correct answer describes cooperative multitasking: the event loop suspends the coroutine at await and interleaves I/O-bound tasks on a single thread. Distractor A is wrong because Uvicorn does not use multiple Python threads to handle requests; concurrency comes from the loop scheduling coroutines, not from threading or GIL behavior.

   Read the full bite: How does Uvicorn use asyncio to handle thousands of concurrent connections?

7. Question 7 of 30

   In FastAPI, why should an async database session dependency wrap yield in try and place await session.close() in finally?

   • aIt guarantees cleanup runs even if the path operation raises an exception.
   • bIt ensures the session closes before FastAPI sends the HTTP response.
   • cIt prevents FastAPI from executing the dependency in a thread pool.
   • dIt allows yielding multiple sessions within a single request lifecycle.
   Show the answer

   Answer: a · It guarantees cleanup runs even if the path operation raises an exception.

   A try/finally block guarantees that await session.close() runs even when the path operation raises an exception, preventing database connection leaks. The thread pool issue in distractor C is caused by using def instead of async def, not by omitting exception handling.

   Read the full bite: Implement an async database session dependency using yield for setup and teardown

8. Question 8 of 30

   In Pydantic v2, what is the runtime validation behavior of a generic wrapper field typed as T when the model is used without parametrization?

   • aThe field is validated as Any, accepting arbitrary data and generating an overly permissive schema
   • bThe TypeVar is inferred from the first assigned value and fixed for subsequent instances
   • cPydantic raises a ValidationError because an unbound TypeVar cannot be validated
   • dPython's static type checker enforces parametrization before Pydantic ever runs validation
   Show the answer

   Answer: a · The field is validated as Any, accepting arbitrary data and generating an overly permissive schema

   The card explicitly states that unparametrized TypeVars are treated as Any at validation time, yielding an overly permissive OpenAPI schema. Option C is a tempting distractor because developers often assume missing generic parameters cause runtime errors, but Pydantic v2 gracefully falls back to Any instead.

   Read the full bite: Create a generic Pydantic BaseModel for API response wrappers

9. Question 9 of 30

   When an incoming GET request reaches a FastAPI app, how does @app.get("/") enable the correct function to run?

   • aIt delays route binding until the first request arrives to improve startup speed.
   • bIt acts as pure syntax sugar to make the code look cleaner without changing framework behavior.
   • cIt wraps the function so it can parse HTTP requests directly at runtime.
   • dIt registers the function in the app's route table at import time and generates OpenAPI metadata.
   Show the answer

   Answer: d · It registers the function in the app's route table at import time and generates OpenAPI metadata.

   The decorator actively registers the function for GET / in the app's internal route table when the module is imported and simultaneously populates the OpenAPI schema, enabling the ASGI layer to dispatch matching requests. Calling it pure syntax sugar is a common misconception because it fundamentally alters the application's routing registry and automatic documentation rather than leaving framework behavior unchanged.

   Read the full bite: What is the purpose of @app.get("/") in FastAPI?

10. Question 10 of 30

    Which approach correctly defines a FastAPI endpoint that captures an integer item_id from a URL like /items/42?

    • aUse app.get("/items/{item_id}") and define async def read_item(item_id: int): then use item_id directly inside the function
    • bUse app.get("/items/{item_id}") and define async def read_item(id: int): then use id directly inside the function
    • cUse app.get("/items/item_id") and define async def read_item(item_id: int): then read request.path_params["item_id"] inside the function
    • dUse app.get("/items/<item_id>") and define def read_item(item_id): then manually cast with int(item_id) inside the function
    Show the answer

    Answer: a · Use app.get("/items/{item_id}") and define async def read_item(item_id: int): then use item_id directly inside the function

    FastAPI binds curly-braced path segments to function arguments with matching names and type hints, automatically converting and injecting the value. Option B is tempting because the route syntax is correct, but the mismatched argument name breaks the default binding unless you use a Path alias.

    Read the full bite: How do you define and access a FastAPI path parameter?

11. Question 11 of 30

    A FastAPI endpoint declares a path parameter as item_id: int. A request arrives for /items/abc, which cannot be coerced to an integer. What happens?

    • aFastAPI returns an automatic 422 error describing the invalid value, and the endpoint function body never executes
    • bPython raises a TypeError at the point where item_id is used inside the function body
    • cFastAPI passes the string abc through unchanged, and the function must convert it manually
    • dThe server returns a 500 Internal Server Error because the type coercion crashes the process
    Show the answer

    Answer: a · FastAPI returns an automatic 422 error describing the invalid value, and the endpoint function body never executes

    FastAPI validates the coerced type before your function runs, so an uncoercible value short circuits into an automatic 422 with no handler code executing. Python itself does nothing with the int annotation at runtime, which is why expecting a TypeError or manual parsing misses the point of the hint.

    Read the full bite: How FastAPI uses type hints for validation

12. Question 12 of 30

    Which FastAPI endpoint declaration identifies a user in the URL path and accepts an optional search term after the question mark?

    • aRoute /users/{user_id}/items with user_id in path and q optional
    • bRoute /users/items with user_id and q as query arguments, q optional
    • cRoute /users/{user_id}/items/{q} with user_id and q in path, q optional
    • dRoute /users/{user_id}/items with user_id in path and q required
    Show the answer

    Answer: a · Route /users/{user_id}/items with user_id in path and q optional

    Option A correctly places the user identifier in the path template and gives q a default of None so it becomes an optional query parameter. Option B is tempting because it also makes q optional, but it omits user_id from the path so FastAPI treats it as a query parameter, violating RESTful hierarchy.

    Read the full bite: What is the difference between a path parameter and a query parameter?

13. Question 13 of 30

    An endpoint declares a query parameter as q: Optional[str] with no default value. What happens when a client omits it?

    • aFastAPI automatically assigns None as the default based solely on the Optional type hint
    • bFastAPI follows Pydantic model conventions and defaults the parameter to None
    • cFastAPI returns a 422 Unprocessable Entity error
    • dFastAPI allows the request and sets q to None because the type hint permits None
    Show the answer

    Answer: c · FastAPI returns a 422 Unprocessable Entity error

    FastAPI derives requirement from the presence or absence of a Python signature default, not from type hints, so Optional[str] without = None is still required and omitting it triggers a 422 error. Option D is a common misconception because Optional alone does not make a parameter optional in FastAPI.

    Read the full bite: How does FastAPI distinguish required optional and default query parameters

14. Question 14 of 30

    What combination of standard and code sources enables FastAPI's automatic interactive documentation?

    • aIt generates a static JSON schema at build time from explicit annotations only
    • bIt requires manually maintaining an openapi.yaml file alongside your code changes
    • cIt uses the Swagger UI standard to parse docstrings without needing type hints
    • dIt dynamically builds an OpenAPI schema from type hints, Pydantic models, decorators, and docstrings
    Show the answer

    Answer: d · It dynamically builds an OpenAPI schema from type hints, Pydantic models, decorators, and docstrings

    FastAPI dynamically generates an OpenAPI schema by extracting metadata from type hints, Pydantic models, decorators, and docstrings, so no manual schema file is required. Option B is wrong because maintaining a separate openapi.yaml by hand is unnecessary and contradicts FastAPI's design.

    Read the full bite: What standard and code elements power FastAPI's auto-generated API docs?

15. Question 15 of 30

    A client sends GET /items?limit=foo to an endpoint with parameter limit: int. What is FastAPI's default response?

    • aHTTP 422 Unprocessable Entity with a JSON body whose detail array contains objects with loc, msg, and type fields
    • bHTTP 422 Unprocessable Entity with a JSON body containing a single detail string describing the error
    • cHTTP 500 Internal Server Error because the int conversion raises an uncaught ValueError
    • dHTTP 400 Bad Request with a plain text error message stating the parameter is invalid
    Show the answer

    Answer: a · HTTP 422 Unprocessable Entity with a JSON body whose detail array contains objects with loc, msg, and type fields

    FastAPI relies on Pydantic to automatically validate query parameters and returns a 422 Unprocessable Entity with a JSON detail array of objects containing loc, msg, and type fields. Option B is tempting because the status code is correct, but the body structure is actually a detailed array rather than a single string.

    Read the full bite: FastAPI non-integer query param default behavior

16. Question 16 of 30

    To accept multiple values for a single query key and allow per-item length constraints in current FastAPI, which parameter declaration should you use?

    • atag: list[str] = Query(default=[])
    • btag: str = Query(...)
    • ctag: Annotated[list[str], Query()] = []
    • dtag: list[str] = []
    Show the answer

    Answer: c · tag: Annotated[list[str], Query()] = []

    Annotated[list[str], Query()] = [] is the modern pattern that separates validation metadata from the default value, enabling per-item constraints. tag: list[str] = Query(default=[]) is the outdated alternative that mixes the default with validation metadata.

    Read the full bite: How do you type-hint repeated query params in FastAPI?

17. Question 17 of 30

    Which statement accurately describes the behavior of {file_path:path} compared to {file_path} in a FastAPI route definition?

    • aThe :path suffix is a Pydantic validator that ensures the value is a valid filesystem path before passing it to the endpoint
    • bBoth match nested paths like docs/tutorial.md because FastAPI treats str parameters as greedy by default
    • c{file_path:path} uses a Starlette converter to greedily match slashes across segments while {file_path} stops at the next slash
    • dURL-encoding the slashes in the request allows {file_path} to match nested paths without using the :path converter
    Show the answer

    Answer: c · {file_path:path} uses a Starlette converter to greedily match slashes across segments while {file_path} stops at the next slash

    {file_path:path} relies on Starlette's path converter to consume the rest of the URL including slashes, whereas a plain parameter matches only one segment regardless of the str type hint. Option B is wrong because a str annotation does not make routing greedy, and option A incorrectly confuses a router directive with Pydantic validation.

    Read the full bite: What is the :path converter in FastAPI?

18. Question 18 of 30

    Which method correctly enables automatic JSON body validation in a FastAPI route?

    • aSubclass BaseModel and declare it as the type of a path operation function parameter
    • bSubclass BaseModel and set it as the response_model in the route decorator
    • cUse a standard Python dataclass as the type annotation for the route parameter
    • dCall await request.json() manually and instantiate the Pydantic model inside the handler
    Show the answer

    Answer: a · Subclass BaseModel and declare it as the type of a path operation function parameter

    FastAPI inspects path operation parameter type annotations to automatically parse and validate incoming JSON against a Pydantic BaseModel. Manually calling request.json() bypasses this automatic pipeline, and response_model only defines the outgoing response schema rather than request validation.

    Read the full bite: How do you define a Pydantic model for FastAPI request body validation?

19. Question 19 of 30

    When a FastAPI endpoint receives JSON with extra fields not defined in the Pydantic model, what occurs by default?

    • aPydantic stores the extra fields in __pydantic_extra__ and continues validation
    • bFastAPI returns a 400 Bad Request before Pydantic validates the payload
    • cPydantic raises a ValidationError causing FastAPI to return a 422 response
    • dPydantic silently drops the extra fields and the request succeeds
    Show the answer

    Answer: d · Pydantic silently drops the extra fields and the request succeeds

    By default Pydantic ignores extra fields, silently dropping them so the model instantiates and the request succeeds. Option C is wrong because that strict 422 behavior only happens when you explicitly configure extra to forbid in model_config.

    Read the full bite: How does Pydantic handle extra JSON fields, and how to configure it?

20. Question 20 of 30

    What is the key difference between a Pydantic field defined as name: str = 'guest' and one defined as name: Optional[str] = None?

    • aOmitting the second field leaves it absent from the model instance.
    • bThe first field is required, while the second is optional.
    • cPassing None to the first field causes it to fall back to 'guest'.
    • dThe first rejects None while the second accepts it, but both may be omitted from input.
    Show the answer

    Answer: d · The first rejects None while the second accepts it, but both may be omitted from input.

    Both fields have defaults so neither is required, yet str = 'guest' rejects None while Optional[str] = None accepts it. Distractor A is tempting because Optional sounds optional, but requiredness is determined solely by the presence or absence of a default.

    Read the full bite: What is the difference between a Pydantic default and Optional field?

21. Question 21 of 30

    In Pydantic V2, how should you enforce a positive price and a regex-formatted SKU without writing custom validators?

    • aSupply the constraints through json_schema_extra so they apply at runtime validation
    • bSet price: float = Field(gt=0) and sku: str = Field(pattern=r'^ITEM-\d{5}$') on standard types
    • cUse conint(gt=0) for price and constr(pattern=r'^ITEM-\d{5}$') for SKU in the model definition
    • dAdd @field_validator methods for price and sku to check values during model validation
    Show the answer

    Answer: b · Set price: float = Field(gt=0) and sku: str = Field(pattern=r'^ITEM-\d{5}$') on standard types

    Field's built-in gt and pattern parameters enforce constraints natively without extra code, while @field_validator adds unnecessary boilerplate and ignores Pydantic V2's native capabilities.

    Read the full bite: Enforce positive price and SKU format using Pydantic Field without custom validators

22. Question 22 of 30

    You are writing a Pydantic v2 model with a name field that must be at least 3 characters. Which implementation is correct?

    • aRun the length check in the FastAPI endpoint before creating the model.
    • bUse @field_validator('name') on a classmethod that raises ValueError without returning the value.
    • cUse @field_validator('name') on a classmethod that raises ValueError and returns the value.
    • dUse @validator('name') on an instance method that sets self.name after checking.
    Show the answer

    Answer: c · Use @field_validator('name') on a classmethod that raises ValueError and returns the value.

    Pydantic v2 requires single-field validators to use @field_validator on a classmethod, raise ValueError on failure, and return the value so processing continues. Option B is tempting but wrong because omitting the return breaks the model lifecycle, and D uses the deprecated v1 pattern.

    Read the full bite: Implement a custom validator for a single Pydantic model field

23. Question 23 of 30

    A FastAPI endpoint returns a UserDB model containing password_hash. Which strategy best prevents exposing the hash while keeping the API contract explicit and maintainable?

    • aSet password_hash to None in the endpoint body before returning the UserDB instance.
    • bUse response_model_exclude={"password_hash"} on the decorator to omit the field during serialization.
    • cCreate a separate UserOut model without password_hash and set response_model=UserOut on the endpoint.
    • dCall user.model_dump(exclude={"password_hash"}) and return the raw dict from the endpoint.
    Show the answer

    Answer: c · Create a separate UserOut model without password_hash and set response_model=UserOut on the endpoint.

    A dedicated output model declaratively isolates the API contract from the database schema and prevents accidental leaks if new sensitive fields are added later. Option B is a tempting quick fix, but it keeps the sensitive field in the source model and hides the contract outside the type system, making it harder to maintain.

    Read the full bite: How do you prevent password_hash from appearing in a FastAPI response?

24. Question 24 of 30

    An Order model needs to hold a list of Product models. What is the idiomatic Pydantic way to ensure nested dictionaries are validated and coerced automatically?

    • aParse the payload with a JSON library first, then pass the resulting objects to Order to avoid nested validation overhead.
    • bAnnotate the field as list[Product] where Product subclasses BaseModel, relying on Pydantic's recursive schema resolution.
    • cDefine products as list and attach a field_validator that manually constructs Product instances from each dictionary.
    • dOverride __init__ in Order to iterate over the raw list and build Product objects before assignment.
    Show the answer

    Answer: b · Annotate the field as list[Product] where Product subclasses BaseModel, relying on Pydantic's recursive schema resolution.

    Annotating the field as list[Product] leverages Pydantic's core schema generation to recursively validate nested items and report precise error paths automatically. Overriding __init__ to manually build Product instances is a red flag because it ignores Pydantic's built-in recursive validation machinery.

    Read the full bite: Model an Order with a nested Product list in Pydantic

25. Question 25 of 30

    In Pydantic v2, which validator configuration correctly enforces that end_date is after start_date while ensuring both fields are already parsed and coerced?

    • aA wrap validator on end_date that accesses the partially constructed model to compare the two date fields
    • bA field validator on end_date that references the sibling start_date field directly from the validation context
    • cA model_validator with mode='after' that compares self.end_date and self.start_date and raises ValueError on violation
    • dA model_validator with mode='before' that manually parses both fields from the raw dict before type coercion occurs
    Show the answer

    Answer: c · A model_validator with mode='after' that compares self.end_date and self.start_date and raises ValueError on violation

    A model_validator with mode='after' receives the fully constructed instance with coerced datetime objects, making cross-field comparison type-safe and reliable. A model_validator with mode='before' is tempting but forces you to handle raw, unparsed input instead of validated types.

    Read the full bite: Ensure end_date is after start_date in Pydantic

26. Question 26 of 30

    You define a User model inheriting from BaseModel with name: str and age: int. What missing step lets FastAPI automatically validate an incoming JSON request body against it?

    • aAdd response_model=User to the route decorator so FastAPI validates incoming data against it
    • bManually parse request.json() and instantiate User inside the route function
    • cDefine User as a Python dataclass and type-hint the parameter with that class
    • dType-hint a path operation parameter with User, e.g., async def create_user(user: User)
    Show the answer

    Answer: d · Type-hint a path operation parameter with User, e.g., async def create_user(user: User)

    FastAPI treats a parameter type-hinted with a BaseModel subclass as the request body and validates it automatically. Option B is tempting but wrong because parsing the body manually with request.json() skips Pydantic validation and prevents OpenAPI documentation generation.

    Read the full bite: How do you define a Pydantic model and use it in FastAPI?

27. Question 27 of 30

    What happens to extra fields on an object returned by a FastAPI endpoint when a response_model is declared?

    • aFastAPI raises a validation error if the returned object contains undeclared fields
    • bFastAPI silently filters out fields not present in the response_model during serialization
    • cThe endpoint must manually remove extra fields before returning to avoid errors
    • dThe response_model must explicitly set extra='allow' to include additional fields
    Show the answer

    Answer: b · FastAPI silently filters out fields not present in the response_model during serialization

    The card explains that FastAPI automatically drops undeclared fields when serializing against the response_model. Option C represents the brittle manual approach the card explicitly warns against, whereas the response_model is designed to handle filtering for you.

    Read the full bite: How would you use a Pydantic response_model to enforce output structure?

28. Question 28 of 30

    In FastAPI, what primarily determines whether a function parameter is treated as a path parameter instead of a query parameter?

    • aThe parameter's position before any parameters that have default values
    • bIf the parameter name appears inside braces in the route path string
    • cExplicitly annotating the parameter with Path from the fastapi module
    • dWhether the parameter has a default value in the function signature
    Show the answer

    Answer: b · If the parameter name appears inside braces in the route path string

    FastAPI inspects the route path template and treats parameters named in braces as path variables, while all others become query parameters. Although Path() can add metadata, it is not required for basic inference, and default values determine optionality rather than parameter location.

    Read the full bite: Define a FastAPI endpoint with path and query parameters

29. Question 29 of 30

    A FastAPI endpoint with parameters file: UploadFile and user_id: str fails when receiving multipart form-data. What is the most likely reason?

    • aFastAPI assumes both parameters come from the JSON body because they lack File() and Form() metadata.
    • bThe endpoint must use bytes instead of UploadFile to enable spooled temporary file handling.
    • cString fields in multipart requests must always be defined as Pydantic models rather than plain types.
    • dpython-multipart is not installed, so FastAPI silently falls back to JSON parsing for binary data.
    Show the answer

    Answer: a · FastAPI assumes both parameters come from the JSON body because they lack File() and Form() metadata.

    Without File() or Form() metadata, FastAPI assumes parameters arrive in the JSON body, which breaks multipart form-data parsing. Distractor A reverses the memory behavior: UploadFile spools large files, while bytes loads everything into memory.

    Read the full bite: Implement a FastAPI file upload endpoint with form data

30. Question 30 of 30

    In FastAPI, which approach best implements a reusable current-user check that preserves OpenAPI docs integration and keeps endpoints testable?

    • aDefine a get_current_user dependency that uses OAuth2PasswordBearer, validates the token, returns a User model, and inject it into routes with Depends.
    • bReturn the raw token string from a dependency and let each endpoint query the database for the user.
    • cManually read request.headers.get("Authorization") inside every endpoint and decode the JWT there.
    • dCreate a middleware that parses the Authorization header and stores the user in request.state.
    Show the answer

    Answer: a · Define a get_current_user dependency that uses OAuth2PasswordBearer, validates the token, returns a User model, and inject it into routes with Depends.

    A dedicated get_current_user dependency keeps auth explicit in the signature, auto-documents security in OpenAPI, and enables test overrides via app.dependency_overrides. Middleware hides the dependency from the docs and complicates testing.

    Read the full bite: How do you create a reusable current-user dependency in FastAPI?