Request body declaration in FastAPI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of declaring a request body in FastAPI?

Declaring a request body in FastAPI tells the framework to expect data sent by the client in the body of the HTTP request, usually in JSON format. This data is then automatically converted into Python objects for easy use.

beginner

How do you declare a request body using Pydantic models in FastAPI?

You create a Pydantic model class with fields representing the expected data, then use it as a function parameter in your path operation. FastAPI reads the request body and converts it into an instance of that model.

intermediate

What happens if the request body does not match the declared Pydantic model in FastAPI?

FastAPI automatically validates the data. If the data is missing required fields or has wrong types, FastAPI returns a clear error response with status code 422 and details about what is wrong.

intermediate

Can you declare multiple request bodies in a single FastAPI endpoint?

No, FastAPI allows only one request body per endpoint. To send multiple pieces of data, you combine them into one Pydantic model or use other parts of the request like query parameters or headers.

intermediate

How do you make a request body field optional in FastAPI?

In the Pydantic model, you use typing.Optional or set a default value for the field. This tells FastAPI that the field is not required in the incoming JSON data.

In FastAPI, how do you tell the framework to expect JSON data in the request body?

A By declaring a Pydantic model as a parameter in the path operation function

B By adding a query parameter

C By setting a header manually

D By using a cookie

FastAPI uses Pydantic models declared as function parameters to parse and validate JSON request bodies.

What status code does FastAPI return if the request body validation fails?

A 400 Bad Request

B 422 Unprocessable Entity

C 500 Internal Server Error

D 404 Not Found

FastAPI returns 422 Unprocessable Entity when the request body data does not match the expected Pydantic model.

Which Python library does FastAPI use to define request body schemas?

A Dataclasses

B Marshmallow

C Pydantic

D SQLAlchemy

FastAPI uses Pydantic to define data models for request bodies and perform validation.

Can you have two separate request bodies in one FastAPI endpoint?

A Yes, by declaring two Pydantic models as parameters

B Yes, if you use query parameters

C Yes, if you use headers

D No, only one request body is allowed per endpoint

FastAPI supports only one request body per endpoint. Multiple data pieces should be combined or sent via other request parts.

How do you make a field optional in a FastAPI request body model?

A Use typing.Optional or provide a default value in the Pydantic model

B Leave the field out of the model

C Set the field to None in the request

D Use a query parameter instead

Using typing.Optional or default values in Pydantic models makes fields optional in FastAPI request bodies.

Explain how to declare and use a request body in FastAPI using Pydantic models.

Think about how you tell FastAPI what data to expect and how it checks it.

Describe what happens when the incoming request body does not match the declared Pydantic model in FastAPI.

Consider how FastAPI helps keep your app safe from bad data.