Pydantic model definition in FastAPI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Pydantic model in FastAPI?

A Pydantic model is a Python class used to define data shapes and validate data automatically. It helps FastAPI check and convert incoming data to the right types.

beginner

How do you define a simple Pydantic model with a name (string) and age (integer)?

You create a class that inherits from BaseModel and add attributes with types, like this:<br><pre>from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int</pre>

beginner

What happens if data sent to a Pydantic model does not match the expected type?

Pydantic raises a validation error. FastAPI will return a clear error message to the user explaining what is wrong with the data.

intermediate

Can Pydantic models have default values? How?

Yes. You assign a default value to the attribute in the class. For example:<br><pre>class Person(BaseModel):
    name: str
    age: int = 30  # default age</pre>

beginner

Why use Pydantic models in FastAPI instead of plain Python dictionaries?

Pydantic models automatically check data types, convert data, and provide clear error messages. This makes your API safer and easier to maintain.

Which base class do you inherit from to create a Pydantic model?

A BaseModel

B ModelBase

C DataModel

D PydanticBase

Pydantic models always inherit from BaseModel to get validation and parsing features.

What type of error does Pydantic raise if data is invalid?

A ValueError

B TypeError

C ValidationError

D SyntaxError

Pydantic raises ValidationError when data does not match the model's expected types.

How do you specify a default value for a field in a Pydantic model?

A Assign it directly in the class attribute

B Use a decorator

C Set it in the constructor

D Use a separate config file

Default values are set by assigning them directly to the class attribute.

Which of these is a valid Pydantic model field declaration?

A age = int: 25

B age: int = 25

C int age = 25

D age: 'int' = '25'

The correct syntax is 'field_name: type = default_value'.

What benefit does Pydantic provide in FastAPI?

A Automatic HTML rendering

B Faster database queries

C Improved CSS styling

D Automatic data validation and conversion

Pydantic helps FastAPI by validating and converting data automatically.

Explain how to create a Pydantic model for a user with email (string) and is_active (boolean) fields.

Think about class syntax and type annotations.

Describe what happens when FastAPI receives data that does not match a Pydantic model's expected types.

Focus on validation and error handling.

Practice

1. What is the main purpose of defining a Pydantic model in FastAPI?

easy

A. To specify the expected data structure and validate incoming data automatically

B. To create database tables automatically

C. To style the API responses with CSS

D. To handle user authentication

Solution

1. Step 1: Understand Pydantic model role

   Pydantic models define the shape and types of data expected in requests or responses.

2. Step 2: Identify validation feature

   They automatically check if incoming data matches the defined types and structure.

3. Final Answer:

   To specify the expected data structure and validate incoming data automatically -> Option A

4. Quick Check:

   Data validation = C [OK]

Hint: Pydantic models shape and check data automatically [OK]

Common Mistakes:

• Confusing Pydantic models with database models
• Thinking Pydantic handles styling or authentication
• Assuming Pydantic models run code logic

2. Which of the following is the correct way to define a Pydantic model with a required string field name and an optional integer field age?

easy

A. class User(BaseModel): name: str; age: int = None

B. class User(BaseModel): name: str; age: Optional[int] = None

C. class User(BaseModel): name = str; age = int = None

D. class User(BaseModel): name: str = None; age: int

Solution

1. Step 1: Check required and optional fields syntax

   Required fields have type only, optional fields use Optional[type] with default None.

2. Step 2: Identify correct syntax

   class User(BaseModel): name: str; age: Optional[int] = None uses name: str (required) and age: Optional[int] = None (optional), which is correct.

3. Final Answer:

   class User(BaseModel): name: str; age: Optional[int] = None -> Option B

4. Quick Check:

   Optional fields use Optional[type] = None [OK]

Hint: Use Optional[type] = None for optional fields [OK]

Common Mistakes:

• Using plain type with default None without Optional
• Assigning type with equals sign incorrectly
• Setting required fields with default None

3. Given this Pydantic model:

from pydantic import BaseModel

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

item = Item(id='5', name='Book', price=12.99)

What will be the type of item.id after creation?

medium

A. TypeError raised

B. str with value '5'

C. float with value 5.0

D. int with value 5

Solution

1. Step 1: Understand Pydantic type coercion

   Pydantic automatically converts compatible types to the declared type if possible.

2. Step 2: Analyze input and conversion

   The string '5' can be converted to int 5, so item.id becomes int 5.

3. Final Answer:

   int with value 5 -> Option D

4. Quick Check:

   Pydantic converts compatible types automatically [OK]

Hint: Pydantic converts strings to int if possible [OK]

Common Mistakes:

• Assuming input type must match exactly without conversion
• Expecting a TypeError for string input
• Thinking the value stays as string

4. What is wrong with this Pydantic model definition?

from pydantic import BaseModel

class Product(BaseModel):
    name: str
    price: float = 'free'

medium

A. Missing import for Optional

B. Class must inherit from object, not BaseModel

C. Default value 'free' is a string but price expects a float

D. Field names cannot be lowercase

Solution

1. Step 1: Check field types and default values

   The field price expects a float but is given a string default 'free'.

2. Step 2: Understand Pydantic validation

   Pydantic will raise a validation error because 'free' cannot be converted to float.

3. Final Answer:

   Default value 'free' is a string but price expects a float -> Option C

4. Quick Check:

   Default value type must match field type [OK]

Hint: Default values must match declared field types [OK]

Common Mistakes:

• Assigning default values of wrong type
• Confusing inheritance requirements
• Thinking field names have naming restrictions

5. You want to create a Pydantic model User with a required username (string), an optional email (string), and a roles list of strings that defaults to an empty list. Which is the correct way to define it?

hard

A. class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = Field(default_factory=list)

B. class User(BaseModel): username: str; email: str = None; roles: list[str] = []

C. class User(BaseModel): username: str; email: Optional[str]; roles: list[str] = None

D. class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = []

Solution

1. Step 1: Identify optional email field syntax

   Email is optional, so use Optional[str] = None to allow missing value.

2. Step 2: Handle mutable default for roles list

   Using roles: list[str] = [] is unsafe because lists are mutable defaults shared across instances. Use Field(default_factory=list) to create a new list each time.

3. Final Answer:

   class User(BaseModel): username: str; email: Optional[str] = None; roles: list[str] = Field(default_factory=list) -> Option A

4. Quick Check:

   Use Field(default_factory=list) for mutable defaults [OK]

Hint: Use Field(default_factory=list) for empty list defaults [OK]

Common Mistakes:

• Using mutable empty list as default directly
• Not marking optional fields with Optional and default None
• Assigning None to non-optional list fields