Pydantic model basics in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this Pydantic model validation?

Given the Pydantic model and input data below, what will be the value of user.age after validation?

FastAPI

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int = 18

input_data = {'name': 'Alice'}
user = User(**input_data)
print(user.age)

A Raises ValidationError

B None

C 0

D 18

💡 Hint

Check the default value assigned to the age field in the model.

Since the input data does not provide an age, Pydantic uses the default value 18 defined in the model.

📝 Syntax

intermediate

Which option correctly defines a Pydantic model with an optional string field?

Select the code snippet that correctly defines a Pydantic model with an optional nickname field of type string.

A

from typing import Optional
from pydantic import BaseModel

class User(BaseModel):
    nickname: str | None = None

B

from pydantic import BaseModel

class User(BaseModel):
    nickname: str = Optional[None]

C

from typing import Optional
from pydantic import BaseModel

class User(BaseModel):
    nickname: Optional[str] = None

D

from pydantic import BaseModel

class User(BaseModel):
    nickname: str = None

💡 Hint

Use the typing module's Optional type and assign None as default.

Option C correctly uses Optional[str] with a default of None, which is the recommended way to declare optional fields in Pydantic.

❓ state_output

advanced

What error does this Pydantic model raise on invalid input?

Given the model and input below, what error will be raised when creating the model instance?

FastAPI

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    price: float

input_data = {'id': 'abc', 'price': 10.5}
product = Product(**input_data)

A TypeError

B pydantic.ValidationError

C ValueError

D No error, instance created successfully

💡 Hint

Check the type of the 'id' field and the input value provided.

The 'id' field expects an int, but the input provides a string 'abc', causing Pydantic to raise a ValidationError.

🧠 Conceptual

advanced

How does Pydantic handle extra fields by default?

If you pass extra fields not defined in the Pydantic model, what happens by default?

A Pydantic raises a ValidationError for extra fields

B Extra fields are ignored and not included in the model instance

C Extra fields are included as attributes in the model instance

D Extra fields overwrite existing model fields

💡 Hint

Think about Pydantic's strictness with unexpected data.

By default, Pydantic raises a ValidationError if extra fields are present in the input data that are not defined in the model.

🔧 Debug

expert

Why does this Pydantic model fail to parse nested data correctly?

Examine the code below. Why does Order fail to parse the nested Item data correctly?

FastAPI

from pydantic import BaseModel

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

class Order(BaseModel):
    item: Item

input_data = {'item': {'name': 'Book', 'quantity': '3'}}
order = Order(**input_data)
print(order.item.quantity, type(order.item.quantity))

A Pydantic converts 'quantity' string '3' to int 3, so no failure occurs

B Pydantic raises ValidationError because 'quantity' is a string, not int

C The nested model is missing a Config class to allow parsing

D The input data keys must be uppercase to match model fields

💡 Hint

Pydantic automatically converts compatible types during validation.

Pydantic automatically converts the string '3' to integer 3 when validating the nested Item model, so no error occurs.