Custom request validation in FastAPI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is custom request validation in FastAPI?

Custom request validation in FastAPI means writing your own rules to check if the data sent by the user is correct before using it in your app.

intermediate

How do you create a custom validator for a request field in FastAPI?

You create a Pydantic model and use the @validator decorator on a method to add your own checks for a field's value.

beginner

What happens if custom validation fails in FastAPI?

FastAPI automatically returns a clear error message with status code 422 to the user, showing what went wrong.

intermediate

Why use custom request validation instead of only relying on built-in types?

Built-in types check basic things like type and length, but custom validation lets you check complex rules like format, ranges, or relationships between fields.

beginner

Show a simple example of a custom validator in FastAPI using Pydantic.

from pydantic import BaseModel, validator

class User(BaseModel):
    name: str
    age: int

    @validator('age')
    def age_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('Age must be positive')
        return v

What decorator is used to add custom validation logic to a Pydantic model field?

A @field_validator

B @validator

C @check

D @validate

The @validator decorator is used in Pydantic models to add custom validation for fields.

If a custom validation fails in FastAPI, what HTTP status code is returned by default?

A 200

B 400

C 422

D 500

FastAPI returns status code 422 Unprocessable Entity when validation fails.

Which library does FastAPI use for request data validation?

A Pydantic

B Marshmallow

C Cerberus

D Voluptuous

FastAPI uses Pydantic for data validation and settings management.

What is a benefit of custom request validation in FastAPI?

A It allows checking complex rules beyond simple types

B It makes the app slower

C It removes the need for type hints

D It disables automatic error messages

Custom validation lets you enforce complex rules that built-in types cannot check.

Where do you define custom validation logic in FastAPI?

A In database models

B Inside route functions

C In middleware

D In Pydantic model classes

Custom validation logic is defined inside Pydantic model classes using validators.

Explain how to add a custom validation rule for a request field in FastAPI using Pydantic.

Think about how Pydantic models handle validation.

Describe what happens when a request fails custom validation in FastAPI.

Consider the user experience when sending bad data.