Field types and constraints in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this FastAPI endpoint with constrained fields?

Consider this FastAPI model and endpoint. What will be the response when sending {"age": 25, "name": "Anna"}?

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()

class User(BaseModel):
    name: str = Field(min_length=3, max_length=10)
    age: int = Field(gt=18, lt=30)

@app.post("/user")
async def create_user(user: User):
    return {"message": f"User {user.name} aged {user.age} created"}

A 422 Unprocessable Entity error due to name length

B 422 Unprocessable Entity error due to age constraint

C {"message": "User Anna aged 25 created"}

D 500 Internal Server Error

💡 Hint

Check the constraints on name and age fields carefully.

The name "Anna" has length 4, which is between 3 and 10. The age 25 is greater than 18 and less than 30. So the input passes validation and the endpoint returns the success message.

📝 Syntax

intermediate

Which option correctly defines a constrained string field in FastAPI?

You want a string field 'title' that must be at least 5 characters and at most 20 characters. Which code snippet is correct?

A title: str = Field(size=(5,20))

B title: str = Field(length=(5,20))

C title: str = Field(min=5, max=20)

D title: str = Field(min_length=5, max_length=20)

💡 Hint

Look for the correct parameter names for string length constraints in Pydantic Field.

The correct parameters for string length constraints are min_length and max_length. Other options use invalid parameter names.

🔧 Debug

advanced

Why does this FastAPI model raise a validation error for input {"score": 101}?

Given this model, why does sending {"score": 101} cause a validation error?

FastAPI

from pydantic import BaseModel, Field

class Result(BaseModel):
    score: int = Field(ge=0, le=100)

A Because score must be a string, not an int

B Because 101 is greater than the maximum allowed value 100

C Because Field does not support ge and le parameters

D Because 101 is less than the minimum allowed value 0

💡 Hint

Check the meaning of ge and le parameters in Field.

The parameters ge=0 and le=100 mean the value must be between 0 and 100 inclusive. 101 is outside this range, so validation fails.

❓ state_output

advanced

What is the value of 'user' after parsing this input with the model?

Given this model and input, what will be the value of the 'user' variable?

FastAPI

from pydantic import BaseModel, Field

class User(BaseModel):
    username: str = Field(min_length=3, max_length=8)
    active: bool = Field(default=True)

input_data = {"username": "bob"}
user = User(**input_data)

A User(username='bob', active=True)

B User(username='bob', active=False)

C ValidationError due to missing 'active' field

D User(username='bob') without 'active' attribute

💡 Hint

Check the default value of the 'active' field.

The 'active' field has a default value of True, so if not provided, it will be set to True automatically.

🧠 Conceptual

expert

Which statement about FastAPI field constraints is TRUE?

Select the correct statement about using Field constraints in FastAPI models.

A Field constraints like min_length and gt are enforced at runtime during request validation.

B Field constraints only affect the OpenAPI schema but do not validate input data.

C Field constraints can only be applied to string fields, not numbers.

D Field constraints automatically convert invalid input to the nearest valid value.

💡 Hint

Think about when and how Pydantic validates data in FastAPI.

Field constraints are enforced by Pydantic during runtime validation of incoming data, rejecting invalid inputs with errors.

Practice

1. In FastAPI, what is the main purpose of using Field() in a Pydantic model?

easy

A. To define API routes

B. To create database tables automatically

C. To add validation rules and metadata to model fields

D. To handle HTTP requests directly

Solution

1. Step 1: Understand the role of Pydantic models in FastAPI

   Pydantic models define the data structure and validation for request and response data.

2. Step 2: Identify what Field() adds

   Field() allows adding constraints like min/max length, default values, and descriptions to model fields.

3. Final Answer:

   To add validation rules and metadata to model fields -> Option C

4. Quick Check:

   Field() = validation and metadata [OK]

Hint: Field() is for validation and extra info on model fields [OK]

Common Mistakes:

• Thinking Field() creates routes or handles requests
• Confusing Field() with database table creation
• Assuming Field() is for HTTP methods

2. Which of the following is the correct way to require a string field with a minimum length of 3 in a Pydantic model using Field()?

easy

A. name: str = Field(required=True, length=3)

B. name: str = Field(min=3)

C. name: str = Field(length=3)

D. name: str = Field(min_length=3)

Solution

1. Step 1: Recall the correct parameter for minimum length in Field()

   The correct parameter to set minimum string length is min_length.

2. Step 2: Check each option for correct syntax

   name: str = Field(min_length=3) uses min_length=3, which is correct. Others use invalid or incorrect parameters.

3. Final Answer:

   name: str = Field(min_length=3) -> Option D

4. Quick Check:

   Minimum string length uses min_length [OK]

Hint: Use min_length for string length constraints in Field() [OK]

Common Mistakes:

• Using min instead of min_length for strings
• Using length instead of min_length
• Adding required=True which is not a Field() parameter

3. Given this Pydantic model snippet:

from pydantic import BaseModel, Field

class Item(BaseModel):
    price: float = Field(gt=0)
    name: str = Field(min_length=2, max_length=10)

What happens if you create Item(price=-5, name='A')?

medium

A. Validation error because price is not greater than 0 and name is too short

B. Item is created successfully with given values

C. Validation error only for price, name is accepted

D. Validation error only for name, price is accepted

Solution

1. Step 1: Check the constraints on price

   Price must be greater than 0 (gt=0). Given price is -5, which fails this.

2. Step 2: Check the constraints on name

   Name must have length between 2 and 10. Given name is 'A' (length 1), which fails min_length=2.

3. Final Answer:

   Validation error because price is not greater than 0 and name is too short -> Option A

4. Quick Check:

   Both price and name fail constraints [OK]

Hint: Check all Field constraints; both price and name fail here [OK]

Common Mistakes:

• Assuming only one field causes error
• Ignoring min_length constraint on strings
• Thinking negative price passes gt=0

4. What is wrong with this FastAPI Pydantic model field definition?

from pydantic import BaseModel, Field

class User(BaseModel):
    age: int = Field(min_length=18)

medium

A. Field cannot be used with integers

B. min_length is invalid for integer fields

C. Missing default value for age

D. Age should be a string, not int

Solution

1. Step 1: Understand Field constraints for int type

   Integer fields use gt or ge for minimum value, not min_length.

2. Step 2: Identify the error in the code

   Using min_length on an int field causes a validation error because it applies only to strings or collections.

3. Final Answer:

   min_length is invalid for integer fields -> Option B

4. Quick Check:

   min_length applies to strings, not ints [OK]

Hint: Use gt/ge for numbers, min_length for strings only [OK]

Common Mistakes:

• Using string constraints on numeric fields
• Thinking Field() can't be used with int
• Confusing default value requirement

5. You want to create a FastAPI model for a product with a tags field that must be a list of strings, each string at least 2 characters long. Which is the best way to enforce this using Pydantic Field()?

hard

A. from typing import List
from pydantic import BaseModel, Field, conlist, constr

class Product(BaseModel):
tags: conlist(constr(min_length=2))

B. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2)

C. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_items=2)

D. from typing import List
from pydantic import BaseModel, Field

class Product(BaseModel):
tags: List[str] = Field(min_length=2, max_length=10)

Solution

1. Step 1: Understand the requirement for list of strings with string length constraints

   We need to enforce each string in the list to have at least 2 characters.

2. Step 2: Identify how to apply constraints on list items

   Pydantic's constr can constrain strings, and conlist can constrain lists of constrained types.

3. Step 3: Evaluate options

   from typing import List from pydantic import BaseModel, Field, conlist, constr class Product(BaseModel): tags: conlist(constr(min_length=2)) uses conlist(constr(min_length=2)) which correctly enforces each string's min length inside the list. Others either apply min_length to the list itself or incorrectly.

4. Final Answer:

   Use conlist with constr to enforce string length inside list -> Option A

5. Quick Check:

   conlist(constr(min_length=2)) = correct nested constraints [OK]

Hint: Use conlist with constr for list of strings with length limits [OK]

Common Mistakes:

• Applying min_length to the list instead of strings inside
• Not using constr for string constraints
• Confusing min_items with min_length