Custom request validation in FastAPI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct FastAPI class.

FastAPI

from fastapi import [1]
app = [1]()

Drag options to blanks, or click blank then click option'

AFastAPI

BRequest

CResponse

DDepends

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Request or Response instead of FastAPI

Forgetting to instantiate the app

The FastAPI class is used to create the app instance.

2fill in blank

medium

Complete the code to define a Pydantic model for request validation.

FastAPI

from pydantic import BaseModel

class User(BaseModel):
    name: [1]
    age: int

Drag options to blanks, or click blank then click option'

Aint

Bbool

Cfloat

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using int for name field

Using bool or float for name

The name field should be a string, so use str.

3fill in blank

hard

Fix the error in the route to accept the User model as request body.

FastAPI

@app.post('/users')
async def create_user(user: [1]):
    return {'name': user.name, 'age': user.age}

Drag options to blanks, or click blank then click option'

Adict

BUser

Cstr

Dint

Attempts:

3 left

💡 Hint

Common Mistakes

Using dict instead of User

Using primitive types like str or int

The route parameter should be the Pydantic model User to validate the request body.

4fill in blank

hard

Fill both blanks to add a custom validator that checks age is positive.

FastAPI

from pydantic import BaseModel, field_validator

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

    @field_validator('[1]')
    def check_age(cls, value):
        if value [2] 0:
            raise ValueError('Age must be positive')
        return value

Drag options to blanks, or click blank then click option'

Aage

B>

C<=

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong field name in the validator

Using the wrong comparison operator

The validator targets the 'age' field and raises an error if the value is less than or equal to 0.

5fill in blank

hard

Fill all three blanks to create a route that validates a User and returns a greeting.

FastAPI

@app.post('/greet')
async def greet_user(user: [1]):
    return {'message': f'Hello, [2]! You are [3] years old.'}

Drag options to blanks, or click blank then click option'

AUser

B{user.name}

C{user.age}

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Using dict instead of User for the parameter

Using wrong variable names in the message

The route accepts a User model, then uses user.name and user.age to create the greeting message.

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