List and set validation in FastAPI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output when validating a list of integers with FastAPI?

Consider this FastAPI endpoint that expects a list of integers. What will be the response if the client sends the JSON body: {"numbers": [1, 2, "3", 4]}?

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from typing import List

app = FastAPI()

class NumbersModel(BaseModel):
    numbers: List[int]

@app.post("/validate")
async def validate_numbers(data: NumbersModel):
    return {"sum": sum(data.numbers)}

A Returns {"sum": 10} because "3" is automatically converted to int 3

B Raises a validation error because "3" is a string, not an int

C Returns {"sum": 7} ignoring the string "3" in the list

D Raises a runtime error during sum() because of mixed types

💡 Hint

Pydantic automatically coerces strings like "3" to int 3 in List[int].

Pydantic performs automatic type coercion for compatible values. The string "3" is converted to int 3, so the list validates as [1, 2, 3, 4] and the sum is 10.

📝 Syntax

intermediate

Which code correctly validates a set of unique strings in FastAPI?

You want to accept a set of unique strings in a POST request body. Which of these Pydantic model definitions is correct?

A

class Model(BaseModel):
    items: Set[str]

B

class Model(BaseModel):
    items: set[str]

C

class Model(BaseModel):
    items: list[str]

D

class Model(BaseModel):
    items: Set

💡 Hint

Remember to import the correct type and use proper capitalization.

The correct way is to import Set from typing and use Set[str]. Using lowercase 'set' works in Python 3.9+, but Pydantic recommends typing.Set for clarity. Option A uses lowercase set which is valid in Python 3.9+, but since the question is about correctness in FastAPI with Pydantic, option A is the best practice.

❓ state_output

advanced

What is the output of this FastAPI endpoint when validating a set with duplicates?

Given this endpoint and input, what will be the output? Input JSON: {"tags": ["python", "fastapi", "python"]}

FastAPI

from fastapi import FastAPI
from pydantic import BaseModel
from typing import Set

app = FastAPI()

class TagModel(BaseModel):
    tags: Set[str]

@app.post("/tags")
async def get_tags(data: TagModel):
    return {"count": len(data.tags), "tags": sorted(data.tags)}

A {"count": 3, "tags": ["fastapi", "python", "python"]}

B {"count": 1, "tags": ["python"]}

C Validation error because input is a list, not a set

D {"count": 2, "tags": ["fastapi", "python"]}

💡 Hint

Think about how sets handle duplicates and how Pydantic converts lists to sets.

Pydantic converts the input list to a set, removing duplicates. So the duplicate "python" is removed, leaving two unique tags.

🔧 Debug

advanced

Why does this FastAPI endpoint raise a validation error?

Examine the code and input. Why does validation fail? Input JSON: {"values": [1, 2, 3.5]} Code: from fastapi import FastAPI from pydantic import BaseModel from typing import List app = FastAPI() class ValuesModel(BaseModel): values: List[int] @app.post("/values") async def values_endpoint(data: ValuesModel): return {"sum": sum(data.values)}

A Because List[int] does not accept any numbers

B Because sum() cannot add floats and ints together

C Because 3.5 is a float, not an int, causing validation to fail

D Because the endpoint is missing async keyword

💡 Hint

Check the types inside the list and how Pydantic validates them.

Pydantic expects all items to be ints. A float 3.5 is not an int, so validation fails before the endpoint runs.

🧠 Conceptual

expert

How does Pydantic handle default values for sets in models?

Consider this Pydantic model: class MyModel(BaseModel): items: Set[str] = set() What is a potential problem with this code when used in FastAPI endpoints?

A The code raises a syntax error because set() cannot be used as a default

B All instances share the same default set object, causing unexpected shared state

C FastAPI rejects the model because sets cannot have default values

D Pydantic automatically creates a new set for each instance, so no problem

💡 Hint

Think about how mutable default arguments behave in Python functions and classes.

Using a mutable default like set() directly as a default value causes all instances to share the same set, leading to bugs. The correct way is to use default_factory.