Factory fixtures in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a pytest factory fixture with parameter

What is the output of this pytest test when run?

Consider the factory fixture creates a dictionary with a key 'value' set to the parameter.

PyTest

import pytest

@pytest.fixture
@pytest.mark.parametrize('data_factory', [10], indirect=True)
def data_factory(request):
    return {"value": request.param}

def test_value(data_factory):
    assert data_factory["value"] == 10
    print(f"Value is {data_factory['value']}")

A Value is '10'

B AssertionError

C Value is 10

D TypeError

💡 Hint

Look at how the parameter is passed and used inside the fixture.

The fixture receives the parameter 10 as an integer and returns a dictionary with key 'value' set to 10. The assertion passes and the print outputs 'Value is 10'.

❓ assertion

intermediate

Correct assertion for a factory fixture output

Given a factory fixture that returns a list of user names, which assertion correctly verifies the list contains exactly 3 users?

PyTest

import pytest

@pytest.fixture
def user_factory():
    return ["alice", "bob", "carol"]

def test_users(user_factory):
    # Which assertion is correct here?

A assert len(user_factory) == 3

B assert user_factory.count == 3

C assert user_factory.size() == 3

D assert user_factory.length == 3

💡 Hint

Remember how to get the length of a list in Python.

The correct way to check the number of items in a list is using len(list). The other options use invalid attributes or methods.

🔧 Debug

advanced

Identify the error in this factory fixture usage

What error will this pytest code raise when running the test?

PyTest

import pytest

@pytest.fixture
def number_factory():
    return 5

def test_number(number_factory):
    assert number_factory == 5

@pytest.mark.parametrize('number_factory', [10], indirect=True)
def test_number_param(number_factory):
    assert number_factory == 10

A No error, tests pass

B TypeError

C ValueError

D FixtureLookupError

💡 Hint

Check how the fixture is used with and without parameters.

The fixture 'number_factory' is not designed to accept parameters, but the second test tries to parametrize it indirectly, causing pytest to raise FixtureLookupError.

🧠 Conceptual

advanced

Purpose of factory fixtures in pytest

What is the main advantage of using factory fixtures in pytest?

A To automatically generate test reports in different formats

B To create reusable test data or objects dynamically for multiple tests

C To replace the need for test parametrization entirely

D To speed up test execution by caching results automatically

💡 Hint

Think about why you might want to generate data or objects fresh for each test.

Factory fixtures help create fresh, reusable test data or objects dynamically, improving test isolation and reducing duplication.

❓ framework

expert

Best practice for factory fixture scope and cleanup

Which pytest fixture scope and cleanup method is best for a factory fixture that creates temporary files needed only during each test?

A Use function scope and yield the resource, then cleanup after yield

B Use session scope and cleanup in a finalizer registered with addfinalizer

C Use module scope and cleanup manually inside the test function

D Use class scope and rely on garbage collection for cleanup

💡 Hint

Consider how long the resource is needed and how pytest manages fixture cleanup.

Function scope ensures the fixture runs fresh for each test. Using yield allows cleanup code after the test finishes, ensuring temporary files are removed properly.