Nested models in FastAPI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the BaseModel class from Pydantic.

FastAPI

from pydantic import [1]

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

AModel

BField

CSchema

DBaseModel

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Model' instead of 'BaseModel'.

Trying to import 'Schema' which is not a Pydantic class.

The BaseModel class is imported from Pydantic to create data models in FastAPI.

2fill in blank

medium

Complete the code to define a nested Pydantic model inside another model.

FastAPI

class Address(BaseModel):
    city: str

class User(BaseModel):
    name: str
    address: [1]

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

Astr

BAddress

Cint

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'str' or 'dict' instead of the nested model class.

Using 'int' which is not appropriate here.

The address field in User should be of type Address to nest the model.

3fill in blank

hard

Fix the error in the nested model definition by completing the missing import.

FastAPI

from pydantic import BaseModel
from typing import [1]

class Item(BaseModel):
    name: str

class Order(BaseModel):
    items: [1][Item]

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

AList

BDict

CSet

DTuple

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Dict' which requires two type parameters.

Using 'Set' or 'Tuple' which are not lists.

The List type from typing is needed to define a list of nested Item models.

4fill in blank

hard

Fill both blanks to define a nested model with an optional field.

FastAPI

from typing import [1]

class Profile(BaseModel):
    bio: [2] = None

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

AOptional

Bstr

Cint

DList

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'int' for bio which should be text.

Not using 'Optional' for a field that can be None.

Optional is imported to allow bio to be optional, and its type is str.

5fill in blank

hard

Fill all three blanks to create a nested model with a list of nested items and a required field.

FastAPI

from typing import [1]

class Product(BaseModel):
    id: int
    name: str

class Cart(BaseModel):
    products: [2][[3]]

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

AList

BDict

CProduct

DSet

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Dict' or 'Set' instead of 'List'.

Using a wrong class name instead of 'Product'.

Import List to type the products field as a list of Product models.

Practice

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

easy

A. To represent complex data structures by embedding one model inside another

B. To speed up the API response time

C. To automatically generate HTML pages

D. To replace database models with simpler ones

Solution

1. Step 1: Understand nested models concept

   Nested models allow you to include one Pydantic model inside another to represent complex or hierarchical data.

2. Step 2: Identify the main purpose in FastAPI

   FastAPI uses nested models to validate and structure complex JSON data clearly and maintainably.

3. Final Answer:

   To represent complex data structures by embedding one model inside another -> Option A

4. Quick Check:

   Nested models = complex data representation [OK]

Hint: Nested models = models inside models for complex data [OK]

Common Mistakes:

• Thinking nested models improve speed
• Confusing nested models with HTML generation
• Assuming nested models replace databases

2. Which of the following is the correct way to define a nested model in FastAPI using Pydantic?

easy

A. class User(BaseModel): name: str; address: str

B. class Address(BaseModel): street: str; class User(BaseModel): name: str; address: Address

C. class User(BaseModel): name: str; address = Address()

D. class Address: street: str; class User: name: str; address: Address

Solution

1. Step 1: Check Pydantic model syntax

   Models must inherit from BaseModel and use type annotations for fields.

2. Step 2: Verify nested model usage

   Nested models are declared by using the nested model class as a type annotation for a field.

3. Final Answer:

   class Address(BaseModel): street: str; class User(BaseModel): name: str; address: Address -> Option B

4. Quick Check:

   Nested model as type annotation = correct syntax [OK]

Hint: Use nested model class as field type with BaseModel inheritance [OK]

Common Mistakes:

• Not inheriting from BaseModel
• Assigning nested model instance instead of type
• Using plain classes without BaseModel

3. Given the following FastAPI models:

class Item(BaseModel): name: str; price: float
class Order(BaseModel): id: int; item: Item

What will be the output of Order(id=1, item={'name': 'Book', 'price': 12.5}).item.price?

medium

A. None

B. '12.5'

C. Error: item must be an Item instance

D. 12.5

Solution

1. Step 1: Understand nested model instantiation

   FastAPI/Pydantic automatically converts nested dicts to model instances when creating the parent model.

2. Step 2: Access nested attribute

   Accessing item.price returns the float value 12.5 from the nested Item model.

3. Final Answer:

   12.5 -> Option D

4. Quick Check:

   Nested dict converts to model, attribute accessed = 12.5 [OK]

Hint: Nested dicts auto-convert to models, access attributes normally [OK]

Common Mistakes:

• Expecting string instead of float
• Thinking nested dicts cause errors
• Assuming nested model must be instance, not dict

4. Identify the error in this FastAPI nested model code:

class Profile(BaseModel): age: int
class User(BaseModel): username: str; profile = {"age": 30}

medium

A. Missing type annotation for profile field

B. Profile class must inherit from BaseSettings, not BaseModel

C. Default value for nested model must be a Profile instance, not a dict

D. username field should be Optional[str]

Solution

1. Step 1: Check field type annotations

   All Pydantic model fields require type annotations (: Type) for proper validation, especially for nested models.

2. Step 2: Identify the missing annotation

   The profile field lacks type annotation: it should be profile: Profile = {"age": 30} or profile: Profile = Profile(age=30) to enable nested model validation.

3. Final Answer:

   Missing type annotation for profile field -> Option A

4. Quick Check:

   Type annotation required for nested model fields [OK]

Hint: All model fields must have type annotations: field: Type [OK]

Common Mistakes:

• Using dict instead of model instance as default
• Ignoring type annotations
• Confusing BaseModel with BaseSettings

5. You want to create a FastAPI model for a blog post with nested author info. The author has name and email. The post has title, content, and author. Which code correctly defines these nested models and validates that the author's email is a valid email address?

hard

A. class Author(BaseModel): name: str; email: str
class Post(BaseModel): title: str; content: str; author: Author

@validator('email')
def check_email(cls, v): return v

B. class Author(BaseModel): name: str; email: str
class Post(BaseModel): title: str; content: str; author: dict

C. class Author(BaseModel): name: str; email: EmailStr
class Post(BaseModel): title: str; content: str; author: Author

D. class Author(BaseModel): name: str; email: EmailStr
class Post(BaseModel): title: str; content: str; author: dict

Solution

1. Step 1: Use EmailStr for email validation

   Pydantic provides EmailStr type to validate email addresses automatically.

2. Step 2: Define nested models correctly

   Author model uses EmailStr, Post model nests Author as a field with correct type annotation.

3. Final Answer:

   class Author(BaseModel): name: str; email: EmailStr class Post(BaseModel): title: str; content: str; author: Author -> Option C

4. Quick Check:

   EmailStr + nested Author model = correct validation [OK]

Hint: Use EmailStr type and nested model as field type [OK]

Common Mistakes:

• Using plain str for email without validation
• Setting author field as dict instead of Author model
• Trying to validate email with incorrect validator placement