Pydantic model definition 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, Field

class User(BaseModel):
    name: str
    age: int = Field(default=18, ge=0, le=120)

user = User(name='Alice', age=25)

A Raises a ValidationError

B 18

C None

D 25

💡 Hint

Check the value passed to the model and the default value.

The age field has a default of 18 but since 25 is provided and it meets the constraints (between 0 and 120), the value is set to 25.

📝 Syntax

intermediate

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

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

A

from pydantic import BaseModel

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

B

from pydantic import BaseModel

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

C

from pydantic import BaseModel
from typing import Optional

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

D

from pydantic import BaseModel
from typing import Optional

class User(BaseModel):
    email: str = Optional[str]

💡 Hint

Use the correct typing for optional fields and default values.

Option C correctly imports Optional and sets the default to None. Option C uses valid syntax in Python 3.10+ but lacks a default value, making the field required. Options A and D misuse Optional.

🔧 Debug

advanced

What error does this Pydantic model raise when instantiated?

Consider this model and instantiation code. What error will occur?

FastAPI

from pydantic import BaseModel

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

product = Product(id='abc', price=19.99)

A TypeError: id must be int

B ValidationError: id must be an integer

C No error, product created successfully

D ValueError: price must be float

💡 Hint

Check the type of the id field and the value passed.

The id field expects an integer but a string 'abc' is provided. Pydantic raises a ValidationError listing the type mismatch.

❓ state_output

advanced

What is the output of this Pydantic model with a computed property?

Given the model below, what will item.total_price output?

FastAPI

from pydantic import BaseModel

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

    @property
    def total_price(self) -> float:
        return self.quantity * self.price_per_unit

item = Item(name='Pen', quantity=3, price_per_unit=1.5)
print(item.total_price)

A 4.5

B 3

C 1.5

D Raises AttributeError

💡 Hint

Multiply quantity by price_per_unit.

The total_price property multiplies quantity (3) by price_per_unit (1.5), resulting in 4.5.

🧠 Conceptual

expert

Which option best describes Pydantic's model validation behavior on nested models?

Consider a Pydantic model with a nested model field. Which statement is true about how Pydantic validates nested models?

A Pydantic validates nested models recursively, applying all field validations in nested models automatically.

B Pydantic only validates the top-level model and ignores nested models unless explicitly validated.

C Validation of nested models requires manual calls to their <code>validate()</code> method.

D Nested models are treated as plain dictionaries without validation.

💡 Hint

Think about how Pydantic handles complex data structures.

Pydantic automatically validates nested models recursively. Each nested model's fields are validated according to their definitions without extra manual steps.