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.

Practice

1. What is the main purpose of using @validator in FastAPI models?

easy

A. To connect the model to a database

B. To automatically generate API documentation

C. To add custom rules that check if input data is valid

D. To style the API response format

Solution

1. Step 1: Understand the role of @validator

   The @validator decorator is used to add custom validation logic to model fields in FastAPI.

2. Step 2: Identify the purpose of validation

   Validation ensures that the data meets specific rules before the app processes it, helping keep input correct and safe.

3. Final Answer:

   To add custom rules that check if input data is valid -> Option C

4. Quick Check:

   Custom validation = check input rules [OK]

Hint: Validators check data rules before use [OK]

Common Mistakes:

• Confusing validators with database connectors
• Thinking validators generate docs
• Assuming validators change response style

2. Which of the following is the correct way to define a custom validator for a Pydantic model field named age?

easy

A. @validator('age')\ndef check_age(cls, value):

B. def validate_age(cls, value):

C. @validate('age')\ndef age_validator(cls, value):

D. @validate_field('age')\ndef age_check(cls, value):

Solution

1. Step 1: Recall the correct decorator syntax

   FastAPI uses Pydantic's @validator decorator with the field name as a string argument.

2. Step 2: Match the function definition style

   The validator function must be a class method with parameters cls and value.

3. Final Answer:

   @validator('age')\ndef check_age(cls, value): -> Option A

4. Quick Check:

   Use @validator('field') with cls, value [OK]

Hint: Use @validator('field') decorator for custom checks [OK]

Common Mistakes:

• Using @validate instead of @validator
• Missing the field name in decorator
• Wrong function parameters

3. Given this code snippet, what will be the output if age is set to 15?

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

    @validator('age')
    def check_age(cls, value):
        if value < 18:
            raise ValueError('Must be 18 or older')
        return value

try:
    user = User(age=15)
except Exception as e:
    print(e)

medium

A. Must be 18 or older

B. TypeError because age is int

C. User object with age 15 is created

D. No output, code runs silently

Solution

1. Step 1: Analyze the validator logic

   The validator checks if age is less than 18 and raises a ValueError if true.

2. Step 2: Check the input value

   Since age=15 is less than 18, the validator raises the error.

3. Final Answer:

   Must be 18 or older -> Option A

4. Quick Check:

   Age < 18 triggers ValueError [OK]

Hint: Validator raises error if condition fails [OK]

Common Mistakes:

• Assuming object creates despite validation error
• Confusing ValueError with TypeError
• Ignoring the exception handling block

4. Identify the error in this custom validator code:

from pydantic import BaseModel, validator

class Product(BaseModel):
    price: float

    @validator('price')
    def check_price(value):
        if value <= 0:
            raise ValueError('Price must be positive')
        return value

medium

A. Validator method must return None

B. Missing cls parameter in validator method

C. Price field should be int, not float

D. Validator decorator should be @validate

Solution

1. Step 1: Check validator method signature

   Validator methods must accept cls as the first parameter, then value.

2. Step 2: Identify missing parameter

   The method check_price only has value, missing cls.

3. Final Answer:

   Missing cls parameter in validator method -> Option B

4. Quick Check:

   Validator needs cls, value parameters [OK]

Hint: Validator methods need cls first, then value [OK]

Common Mistakes:

• Omitting cls parameter
• Using wrong decorator name
• Returning None instead of value

5. You want to create a custom validator that ensures a username is lowercase and at least 3 characters long. Which code correctly implements this in a FastAPI Pydantic model?

from pydantic import BaseModel, validator

class User(BaseModel):
    username: str

    @validator('username')
    def validate_username(cls, value):
        # Fill in validation logic here
        pass

hard

A. if len(value) < 3 and not value.islower():\n raise ValueError('Username must be lowercase and 3+ chars')\nreturn value

B. if len(value) < 3 and value.islower():\n raise ValueError('Username too short')\nreturn value

C. if len(value) > 3 and value.islower():\n return value\nelse:\n raise ValueError('Invalid username')

D. if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid')

Solution

1. Step 1: Understand validation requirements

   The username must be lowercase and at least 3 characters long.

2. Step 2: Check each option logic

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') correctly checks both conditions and raises error if not met, returning value otherwise.

3. Final Answer:

   if value.islower() and len(value) >= 3:\n return value\nraise ValueError('Username invalid') -> Option D

4. Quick Check:

   Check both conditions, raise error if false [OK]

Hint: Check all conditions with 'and', raise error if false [OK]

Common Mistakes:

• Using 'or' instead of 'and' causing wrong logic
• Returning value inside else but missing return in if
• Incorrect length comparison operators