Custom request validation in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output when a FastAPI endpoint uses a custom validator that raises a ValueError?

Consider this FastAPI endpoint with a Pydantic model that has a custom validator raising ValueError if a condition fails. What will the client receive if the validation fails?

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, validator

app = FastAPI()

class Item(BaseModel):
    name: str
    quantity: int

    @validator('quantity')
    def quantity_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('Quantity must be positive')
        return v

@app.post('/items/')
async def create_item(item: Item):
    return item

A The client receives a 422 Unprocessable Entity response with a JSON error explaining 'Quantity must be positive'.

B The server crashes with a ValueError and no response is sent to the client.

C The client receives a 500 Internal Server Error with no details.

D The client receives a 200 OK response with the invalid data echoed back.

💡 Hint

Think about how FastAPI handles validation errors raised by Pydantic.

FastAPI uses Pydantic for validation. When a validator raises ValueError, FastAPI catches it and returns a 422 response with details about the validation error.

📝 Syntax

intermediate

Which code snippet correctly implements a custom request validator in FastAPI using Pydantic?

Choose the code that correctly defines a Pydantic model with a custom validator for a field age that must be at least 18.

A

class User(BaseModel):
    age: int

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

B

class User(BaseModel):
    age: int

    def check_age(self):
        if self.age < 18:
            raise ValueError('Must be at least 18')

C

class User(BaseModel):
    age: int

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

D

class User(BaseModel):
    age: int

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

💡 Hint

Remember the correct decorator name and its usage in Pydantic.

The @validator decorator requires the field name as a string argument. Option A correctly uses @validator('age'). Option A uses a wrong decorator name, B is missing the decorator, and A misses the field name argument.

🔧 Debug

advanced

Why does this FastAPI custom validator not run and how to fix it?

Given this Pydantic model, the custom validator does not seem to run when the API receives data. What is the cause?

FastAPI

from pydantic import BaseModel, validator

class Product(BaseModel):
    price: float

    def validate_price(cls, v):
        if v <= 0:
            raise ValueError('Price must be positive')
        return v

A The validator must be a static method, so it needs <code>@staticmethod</code> decorator.

B The method should be named <code>check_price</code> instead of <code>validate_price</code>.

C The method is missing the <code>@validator('price')</code> decorator, so it is never called.

D The validator must return a boolean, not the value.

💡 Hint

Check how Pydantic knows which methods are validators.

In Pydantic, validators must be decorated with @validator('field_name') to be recognized and run. Without the decorator, the method is just a normal method and not called during validation.

❓ state_output

advanced

What is the response when a FastAPI endpoint uses a root validator that modifies data?

Given this Pydantic model with a root validator that changes the full_name field, what will the API return when posting {"first_name": "Jane", "last_name": "Doe"}?

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, root_validator

app = FastAPI()

class User(BaseModel):
    first_name: str
    last_name: str
    full_name: str = ''

    @root_validator
    def set_full_name(cls, values):
        values['full_name'] = f"{values.get('first_name', '')} {values.get('last_name', '')}"
        return values

@app.post('/users/')
async def create_user(user: User):
    return user.dict()

A {"first_name": "Jane", "last_name": "Doe", "full_name": ""}

B 422 Unprocessable Entity error because full_name is empty by default

C 500 Internal Server Error due to root_validator modifying values

D {"first_name": "Jane", "last_name": "Doe", "full_name": "Jane Doe"}

💡 Hint

Think about what a root validator can do to the data before returning it.

The root validator runs after individual field validation and can modify the entire data dictionary. Here it sets full_name by combining first and last names. The API returns the updated data including the computed full_name.

🧠 Conceptual

expert

Which statement best describes how FastAPI handles custom request validation errors?

Choose the statement that correctly explains FastAPI's behavior when a custom validation error occurs in a Pydantic model used for request validation.

A FastAPI ignores validation errors raised by Pydantic and passes the invalid data to the endpoint function.

B FastAPI catches validation errors raised by Pydantic validators and automatically returns a 422 response with detailed error messages in JSON format.

C FastAPI converts all validation errors into 500 Internal Server Errors without details to avoid leaking information.

D FastAPI requires manual try-except blocks in each endpoint to catch validation errors and return responses.

💡 Hint

Recall how FastAPI integrates with Pydantic for validation.

FastAPI automatically handles Pydantic validation errors by returning a 422 status code with a JSON body describing the errors. This behavior is built-in and requires no manual error handling in endpoints.

Practice

1. What is the main purpose of custom request validation in FastAPI?

easy

A. To handle database connections

B. To speed up the API response time

C. To check and enforce rules on incoming data before processing

D. To automatically generate API documentation

Solution

1. Step 1: Understand the role of validation

   Validation ensures data meets expected rules before using it.

2. Step 2: Identify FastAPI's validation purpose

   FastAPI uses validation to keep data clean and safe, preventing bad data.

3. Final Answer:

   To check and enforce rules on incoming data before processing -> Option C

4. Quick Check:

   Validation = Data rules check [OK]

Hint: Validation means checking data correctness before use [OK]

Common Mistakes:

• Confusing validation with performance optimization
• Thinking validation manages database connections
• Mixing validation with documentation generation

2. Which of the following is the correct way to add a custom validator in a Pydantic model for FastAPI?

easy

A. Override the __init__ method of the model

B. Use @validator decorator inside the model class

C. Add a method named validate() outside the model

D. Use @app.validator decorator in the FastAPI app

Solution

1. Step 1: Recall Pydantic validation method

   Pydantic uses @validator decorator inside model classes for custom checks.

2. Step 2: Check other options

   Overriding __init__ or external validate() methods are not standard; @app.validator does not exist.

3. Final Answer:

   Use @validator decorator inside the model class -> Option B

4. Quick Check:

   @validator = Custom check inside model [OK]

Hint: Custom validators use @validator inside Pydantic models [OK]

Common Mistakes:

• Trying to override __init__ for validation
• Using decorators outside the model class
• Confusing FastAPI app decorators with Pydantic validators

3. Given this Pydantic model with a custom validator:

from pydantic import BaseModel, validator

class User(BaseModel):
    age: int

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

What happens if a request sends {"age": -5} to a FastAPI endpoint using this model?

medium

A. The endpoint accepts the data and processes it

B. FastAPI ignores the validator and accepts the data

C. The server crashes with an unhandled exception

D. FastAPI returns a 422 error with validation details

Solution

1. Step 1: Understand the validator logic

   The validator raises ValueError if age is not positive.

2. Step 2: Know FastAPI's error handling

   FastAPI catches validation errors and returns a 422 response with details.

3. Final Answer:

   FastAPI returns a 422 error with validation details -> Option D

4. Quick Check:

   Invalid data triggers 422 error [OK]

Hint: Invalid data triggers 422 error in FastAPI [OK]

Common Mistakes:

• Assuming server crashes on validation error
• Thinking FastAPI accepts invalid data silently
• Confusing 422 with 500 server error

4. Identify the error in this FastAPI custom validation code:

from pydantic import BaseModel, validator

class Item(BaseModel):
    price: float

    @validator('price')
    def check_price(cls, value):
        if value < 0:
            return ValueError('Price must be non-negative')
        return value

medium

A. The validator should raise ValueError, not return it

B. The validator must be a staticmethod, not classmethod

C. The field price should be an int, not float

D. The validator name must start with 'validate_'

Solution

1. Step 1: Check validator error handling

   Validators must raise exceptions, not return them.

2. Step 2: Verify other parts

   Validator is correctly a classmethod; price type float is valid; no naming rule requires 'validate_' prefix.

3. Final Answer:

   The validator should raise ValueError, not return it -> Option A

4. Quick Check:

   Raise errors in validators, don't return [OK]

Hint: Raise exceptions in validators, don't return them [OK]

Common Mistakes:

• Returning exceptions instead of raising
• Confusing @validator with staticmethod
• Changing field types unnecessarily

5. You want to validate a request body in FastAPI where a user can submit either an email or a phone number, but not both. How can you implement this custom validation in a Pydantic model?

hard

A. Use a root_validator to check that exactly one of email or phone is provided

B. Add separate @validator for email and phone fields to check presence

C. Use FastAPI dependency to validate after model parsing

D. Make both fields required and let FastAPI handle conflicts automatically

Solution

1. Step 1: Understand the validation requirement

   We need to ensure exactly one of two fields is present, a cross-field check.

2. Step 2: Choose validation method

   root_validator runs after all fields are parsed and can check multiple fields together.

3. Step 3: Evaluate other options

   Field validators cannot check other fields; dependency validation is after parsing; making both required contradicts the rule.

4. Final Answer:

   Use a root_validator to check that exactly one of email or phone is provided -> Option A

5. Quick Check:

   Cross-field checks use root_validator [OK]

Hint: Use root_validator for checks involving multiple fields [OK]

Common Mistakes:

• Trying to check multiple fields in single field validators
• Relying on FastAPI dependencies for core validation
• Making both fields required when only one should be