Custom validators in FastAPI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom validator in FastAPI?

A custom validator is a user-defined function or method that checks if data meets specific rules before processing. It helps ensure data is correct and safe.

beginner

How do you create a custom validator using Pydantic in FastAPI?

You create a method decorated with @validator inside a Pydantic model class. This method receives the field value and returns the validated or transformed value.

beginner

What is the purpose of the @validator decorator in FastAPI?

The @validator decorator marks a method as a custom validator for one or more fields in a Pydantic model. It runs automatically during data validation.

intermediate

Can custom validators in FastAPI modify the input data? How?

Yes, custom validators can modify input data by returning a changed value. The returned value replaces the original field value during validation.

intermediate

What happens if a custom validator raises a ValueError in FastAPI?

If a ValueError is raised inside a custom validator, FastAPI returns a clear error response to the client explaining the validation failure.

Which decorator is used to create a custom validator in FastAPI's Pydantic models?

A @validator

B @validate

C @check

D @custom

The correct decorator to create custom validators in Pydantic models used by FastAPI is @validator.

What should a custom validator method return?

A Nothing (void)

B An error message string

C A boolean true or false

D The validated or transformed value

Custom validators must return the validated or transformed value to replace the original field value.

If a custom validator raises a ValueError, what does FastAPI do?

A Automatically fixes the data

B Ignores the error and continues

C Returns an error response to the client

D Logs the error but returns success

FastAPI returns an error response explaining the validation failure if a ValueError is raised.

Where do you define custom validators in FastAPI?

A In the main FastAPI app file

B Inside Pydantic model classes

C In the route function

D In the database schema

Custom validators are defined inside Pydantic model classes used for request or response data.

Can a custom validator check multiple fields at once?

A Yes, using <code>@root_validator</code>

B No, only one field at a time

C Yes, using <code>@multi_validator</code>

D No, you must write separate validators

To validate multiple fields together, Pydantic provides @root_validator.

Explain how to create and use a custom validator in FastAPI with Pydantic models.

Think about how Pydantic models check data before FastAPI processes it.

Describe what happens when a custom validator fails in FastAPI.

Consider how FastAPI handles errors to keep data safe.