Custom validators in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What output does this FastAPI Pydantic model produce when invalid data is sent?

Consider this FastAPI Pydantic model with a custom validator. What happens if the input JSON is {"age": 15}?

FastAPI

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

    @validator('age')
    def check_age(cls, v):
        if v < 18:
            raise ValueError('Must be at least 18')
        return v

user = User(age=15)

A Creates User instance with age 15 without error

B Raises ValueError with message 'Must be at least 18'

C Raises TypeError because age is int but validator returns null

D Raises ValidationError from Pydantic with message about age

💡 Hint

Think about how Pydantic handles validation errors inside models.

Pydantic wraps validation errors raised in custom validators into a ValidationError. So when age is less than 18, it raises a ValidationError with details about the 'age' field failing validation.

📝 Syntax

intermediate

Which option correctly defines a Pydantic custom validator for multiple fields?

You want to create a validator that checks two fields together in a Pydantic model. Which code snippet is correct?

FastAPI

from pydantic import BaseModel, root_validator

class Data(BaseModel):
    start: int
    end: int

    @root_validator
    def check_start_end(cls, values):
        start, end = values.get('start'), values.get('end')
        if start >= end:
            raise ValueError('start must be less than end')
        return values

A @root_validator\ndef check_start_end(cls, values):\n start, end = values.get('start'), values.get('end')\n if start >= end:\n raise ValueError('start must be less than end')\n return values

B @root_validator(pre=True)\ndef check(cls, values):\n if values['start'] >= values['end']:\n raise ValueError('start must be less than end')\n return values

C @validator(['start', 'end'])\ndef check(cls, v):\n if v['start'] >= v['end']:\n raise ValueError('start must be less than end')\n return v

D @validator('start', 'end')\ndef check(cls, v):\n if v < 0:\n raise ValueError('Negative not allowed')\n return v

💡 Hint

Use root_validator to validate multiple fields together.

The root_validator decorator is used to validate multiple fields at once. Option A correctly uses @root_validator and accesses values dict to check start and end.

🔧 Debug

advanced

Why does this custom validator not run in FastAPI?

Given this Pydantic model, why does the custom validator not execute when creating an instance?

FastAPI

from pydantic import BaseModel, validator

class Item(BaseModel):
    name: str

    @validator('name')
    def name_must_not_be_empty(cls, value):
        if not value:
            raise ValueError('Name cannot be empty')
        return value

item = Item(name=None)

A Validator does not run because the decorator is missing parentheses

B Validator does not run because null is not allowed by default for str fields

C Validator runs but raises TypeError because null is not a string

D Validator runs and raises ValueError as expected

💡 Hint

Check how Pydantic treats null values for required fields.

By default, Pydantic does not allow null for required fields. Since name is str without Optional, null is invalid and raises a validation error before the validator runs. The validator only runs if the value passes type checks.

❓ state_output

advanced

What is the value of 'email' after validation in this FastAPI model?

This Pydantic model uses a custom validator to normalize emails. What is the value of user.email after creating User(email=' [e-mail] ')?

FastAPI

from pydantic import BaseModel, validator

class User(BaseModel):
    email: str

    @validator('email')
    def normalize_email(cls, v):
        return v.strip().lower()

user = User(email='  [e-mail]  ')
result = user.email

A '[e-mail]'

B ' [e-mail] '

C '[e-mail]'

D Raises ValidationError

💡 Hint

Look at what the validator returns after stripping and lowering.

The validator strips whitespace and converts the email to lowercase, so the final value is '[e-mail]'.

🧠 Conceptual

expert

Which statement about FastAPI custom validators is true?

Select the correct statement about how custom validators work in FastAPI's Pydantic models.

A Custom validators run after type validation and receive already converted Python types.

B Custom validators must always return null to indicate success.

C Custom validators run before type validation and can modify raw input data.

D Custom validators can only validate single fields, not multiple fields together.

💡 Hint

Think about when validators run in Pydantic's validation process.

Custom validators run after Pydantic converts input data to Python types. They receive the converted value and must return the validated or modified value.