Top 30 Fastapi Interview Questions and Answers

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1. Question 1 of 30

   What is the primary mechanism by which Python type hints improve code reliability?

   • aThey enforce strict type checking during the program's runtime execution.
   • bThey automatically convert incorrect data types to the expected types at runtime.
   • cThey enable static analysis tools to identify potential type mismatches before execution.
   • dThey provide a mechanism for validating all external input data at the system's boundaries.
   Show the answer

   Answer: c · They enable static analysis tools to identify potential type mismatches before execution.

   Type hints are ignored by the Python interpreter at runtime. Their primary role is to allow static analysis tools to check for type consistency and potential errors before the code is ever executed, improving reliability. They do not perform runtime validation of external data.

   Read the full bite: Python Type Hints: Documentation Your Linter Can Read

2. Question 2 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
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   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

3. Question 3 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.
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   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?

4. Question 4 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?

5. Question 5 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.
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   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

6. Question 6 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?

7. Question 7 of 30

   When a decorator is applied to a function, what is the immediate effect on that function's definition?

   • aIt modifies the original function's source code to embed new functionality.
   • bIt passes the original function as an argument to the decorator each time the function is called.
   • cIt creates a separate, independent function that is called before the original function.
   • dIt replaces the original function in its scope with a new, wrapped function.
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   Answer: d · It replaces the original function in its scope with a new, wrapped function.

   The card states that the decorator returns a wrapper function, which 'replaces the original my_func in the surrounding scope.' This means the original function's name now refers to the new, wrapped function. Option A is incorrect because decorators add behavior without altering the original function's source code.

   Read the full bite: Python Decorators: Functions that Wrap Functions

8. Question 8 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.
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   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?

9. Question 9 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.
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   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

10. Question 10 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

11. Question 11 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.
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    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?

12. Question 12 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
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    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?

13. Question 13 of 30

    You write an async generator that awaits during resource teardown, but mistakenly decorate it with @contextmanager. What is the most likely result?

    • aThe event loop schedules the cleanup in a background thread to prevent blocking
    • bA runtime error occurs because the synchronous decorator cannot await inside the generator
    • cThe async with statement falls back to __enter__ and __exit__, silently skipping the async cleanup
    • dCleanup runs synchronously and blocks the event loop until the database releases the connection
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    Answer: b · A runtime error occurs because the synchronous decorator cannot await inside the generator

    The card explicitly warns that @contextmanager is for synchronous generators only and cannot await, causing a crash. Distractor A is tempting because blocking the event loop is discussed as a general risk of async code, but the specific mistake of using the sync decorator with async cleanup results in a runtime error rather than silent blocking.

    Read the full bite: Python Async Context Managers

14. Question 14 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
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    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?

15. Question 15 of 30

    What specific problem did direct access to "__annotations__" for a class, like "Derived.__annotations__", commonly cause in Python 3.9 that "inspect.get_annotations()" addresses in Python 3.10+?

    • aIt raised an AttributeError if the class itself did not define any annotations, requiring a default value.
    • bIt failed to resolve string-based forward references, returning them as raw strings instead of evaluated types.
    • cIt would incorrectly show annotations inherited from a parent class, even if the derived class had no explicit annotations.
    • dIt could not access annotations for class methods or static methods, only for instance attributes.
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    Answer: c · It would incorrectly show annotations inherited from a parent class, even if the derived class had no explicit annotations.

    In Python 3.9 and earlier, direct access to a class's __annotations__ would incorrectly include annotations inherited from parent classes. Python 3.10+ and `inspect.get_annotations()` resolve this by ensuring a class's __annotations__ only contains its own explicitly defined annotations, returning an empty dictionary if none are present.

    Read the full bite: Accessing Python Type Annotations Safely

16. Question 16 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
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    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

17. Question 17 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
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    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?

18. Question 18 of 30

    What is the primary reason for creating a single FastAPI instance (e.g., app = FastAPI()) for your application?

    • aIt allows for the seamless integration of third-party libraries and plugins.
    • bIt is required for the automatic generation of interactive API documentation.
    • cIt prevents memory leaks and performance degradation caused by multiple instances.
    • dIt serves as the central manager for all routing, configuration, and lifecycle events.
    Show the answer

    Answer: d · It serves as the central manager for all routing, configuration, and lifecycle events.

    The FastAPI instance acts as the central, authoritative object that orchestrates the entire application, managing all routing, configuration, and lifecycle events. While it does contribute to API documentation, that is a feature derived from its central management role, not its primary purpose for being a single instance.

    Read the full bite: FastAPI Application Instance: Your API's Central Hub

19. Question 19 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

20. Question 20 of 30

    Which scenario represents an inappropriate use of a FastAPI path operation decorator's arguments?

    • aGrouping related API endpoints together in the generated documentation.
    • bSetting the default HTTP status code for a successful response.
    • cDynamically changing the HTTP status code based on specific input conditions.
    • dDeclaring an endpoint as no longer recommended for new development.
    Show the answer

    Answer: c · Dynamically changing the HTTP status code based on specific input conditions.

    FastAPI decorator arguments are designed for static configuration, such as setting a default status code or grouping endpoints. Dynamic logic, like returning different status codes based on request input, should be handled within the endpoint function itself, not via decorator arguments.

    Read the full bite: FastAPI: Configure Endpoints with Decorators

21. Question 21 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?

22. Question 22 of 30

    In FastAPI, what is the primary consequence of defining a path parameter without a type hint (e.g., item_id instead of item_id: int)?

    • aFastAPI will automatically infer the correct data type from the incoming URL segment.
    • bFastAPI will return a 422 Unprocessable Entity error for any request to that endpoint.
    • cThe path parameter will always be treated as a string, regardless of its content.
    • dThe application will fail to start due to a type declaration error.
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    Answer: c · The path parameter will always be treated as a string, regardless of its content.

    The card explicitly states that 'without int, 123 is just a string,' meaning FastAPI treats untyped path parameters as strings by default. FastAPI does not infer types for path parameters; it requires explicit type hints for validation and conversion.

    Read the full bite: Path Parameters: Turning URL Parts into Variables

23. Question 23 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?

24. Question 24 of 30

    For which purpose are FastAPI query parameters most appropriately used?

    • aTo send complex or sensitive data, such as a user's entire profile.
    • bTo provide optional criteria for filtering or paginating a list of items.
    • cTo define a mandatory, structural component of the URL path.
    • dTo identify a specific, single resource within a collection.
    Show the answer

    Answer: b · To provide optional criteria for filtering or paginating a list of items.

    The card states query parameters are for optional data like filtering, pagination, or sorting a collection of resources. Options A and D describe the purpose of path parameters, while option A describes when to use a request body, not query parameters.

    Read the full bite: FastAPI Query Parameters: Beyond the URL Path

25. Question 25 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?

26. Question 26 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?

27. Question 27 of 30

    What is the primary benefit of using Uvicorn workers for a FastAPI application in production?

    • aIt automatically resolves performance issues caused by blocking I/O operations.
    • bIt simplifies debugging by providing clearer error tracebacks.
    • cIt enables automatic code reloading when changes are detected.
    • dIt allows the application to utilize multiple CPU cores for concurrent request processing.
    Show the answer

    Answer: d · It allows the application to utilize multiple CPU cores for concurrent request processing.

    Uvicorn workers are designed to scale your application by running multiple processes, each capable of handling requests, thereby utilizing all available CPU cores to process requests concurrently. Options A and B are incorrect because workers actually complicate debugging and disable auto-reloading; option A is wrong as workers do not fix fundamentally slow or blocking application logic.

    Read the full bite: Uvicorn Workers: Scaling Your FastAPI App

28. Question 28 of 30

    When developing a FastAPI application, for which purpose is a Pydantic BaseModel most effectively utilized?

    • aSpecifying validation rules for parameters embedded directly in the URL path.
    • bDeclaring optional filtering criteria passed as query parameters in a GET request.
    • cTo process simple key-value pairs submitted through an HTML form.
    • dTo define the expected structure and types of data within a POST or PUT request's body.
    Show the answer

    Answer: d · To define the expected structure and types of data within a POST or PUT request's body.

    The card states that Pydantic models are used for 'the request body of any POST, PUT, or PATCH endpoint where the client sends structured data.' Options A, B, and D describe scenarios where Pydantic models are explicitly advised not to be used for the primary request body model, as those are handled by other FastAPI mechanisms like path parameters, query parameters, or Form().

    Read the full bite: FastAPI: Pydantic for Robust Request Bodies

29. Question 29 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?

30. Question 30 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