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.

Practice

1. What is the main purpose of a factory fixture in pytest?

easy

A. To run tests in parallel automatically

B. To create reusable test data with flexible parameters

C. To generate test reports in HTML format

D. To mock external API calls during tests

Solution

1. Step 1: Understand what factory fixtures do

   Factory fixtures return a function that can create test data with different parameters as needed.

2. Step 2: Compare with other options

   Running tests in parallel, generating reports, or mocking APIs are different pytest features, not factory fixtures.

3. Final Answer:

   To create reusable test data with flexible parameters -> Option B

4. Quick Check:

   Factory fixture = reusable flexible test data creator [OK]

Hint: Factory fixtures build test data functions fast [OK]

Common Mistakes:

• Confusing factory fixtures with mocking
• Thinking factory fixtures run tests
• Assuming factory fixtures generate reports

2. Which of the following is the correct way to define a simple factory fixture in pytest?

easy

A. @pytest.fixture
def user_factory(name):
return {'name': name}

B. def user_factory():
return {'name': 'default'}

C. @pytest.fixture
def user_factory():
def create_user(name):
return {'name': name}
return create_user

D. @pytest.fixture
def user_factory():
return {'name': 'default'}

Solution

1. Step 1: Identify factory fixture structure

   A factory fixture returns a function that accepts parameters to create test data dynamically.

2. Step 2: Check each option

   @pytest.fixture def user_factory(): def create_user(name): return {'name': name} return create_user defines a fixture returning a function that takes a name and returns a dict, which is correct. Options A, C, and D do not return a function, so they are not factory fixtures.

3. Final Answer:

   @pytest.fixture def user_factory(): def create_user(name): return {'name': name} return create_user -> Option C

4. Quick Check:

   Factory fixture = fixture returning a function [OK]

Hint: Factory fixtures return a function inside the fixture [OK]

Common Mistakes:

• Returning data directly instead of a function
• Missing @pytest.fixture decorator
• Defining fixture with parameters directly

3. Given the following pytest code, what will be the output of the test?

@pytest.fixture
def number_factory():
    def create_number(x):
        return x * 2
    return create_number

def test_double(number_factory):
    result = number_factory(5)
    assert result == 10
    print(result)

medium

A. Test passes and prints 10

B. Test fails with AssertionError

C. SyntaxError due to fixture usage

D. Test passes but prints 5

Solution

1. Step 1: Understand the factory fixture behavior

   The fixture returns a function that doubles the input number.

2. Step 2: Analyze the test function

   The test calls number_factory(5), which returns 5 * 2 = 10, then asserts result == 10, which is true, so test passes and prints 10.

3. Final Answer:

   Test passes and prints 10 -> Option A

4. Quick Check:

   5 * 2 = 10, assertion true [OK]

Hint: Factory fixture returns function; call it with argument [OK]

Common Mistakes:

• Thinking fixture itself is called with argument
• Expecting print output to fail test
• Confusing assertion logic

4. Identify the error in this factory fixture code and how to fix it:

@pytest.fixture
def item_factory():
    def create_item(name, price):
        return {'name': name, 'price': price}
    return create_item

def test_item(item_factory):
    item = item_factory('Book')
    assert item['price'] == 10

medium

A. The fixture is missing @pytest.mark.parametrize decorator; add it.

B. The fixture should not return a function; fix by returning a dict directly.

C. The test should not use the fixture as a function; fix by removing parentheses.

D. The factory function is missing the 'price' argument; fix by passing price when calling.

Solution

1. Step 1: Check the factory function parameters

   The factory function create_item expects two arguments: name and price.

2. Step 2: Analyze the test call

   The test calls item_factory('Book') with only one argument, missing price, causing an error or wrong data.

3. Final Answer:

   The factory function is missing the 'price' argument; fix by passing price when calling. -> Option D

4. Quick Check:

   Factory args must match call args [OK]

Hint: Match factory function parameters with call arguments [OK]

Common Mistakes:

• Calling factory with fewer arguments than defined
• Returning dict directly instead of function
• Misusing fixture as a simple variable

5. You want to create a factory fixture that builds user dictionaries with optional age and default country='USA'. Which of the following implementations correctly achieves this?

hard

A. @pytest.fixture
def user_factory():
def create_user(name, age=None, country='USA'):
return {'name': name, 'age': age, 'country': country}
return create_user

B. @pytest.fixture
def user_factory(name, age=None, country='USA'):
return {'name': name, 'age': age, 'country': country}

C. @pytest.fixture
def user_factory():
return {'name': 'default', 'age': None, 'country': 'USA'}

D. @pytest.fixture
def user_factory():
def create_user(name, age, country):
return {'name': name, 'age': age, 'country': country}
return create_user

Solution

1. Step 1: Understand factory fixture with optional/default parameters

   The factory fixture should return a function that accepts parameters with defaults for optional values.

2. Step 2: Evaluate each option

   @pytest.fixture def user_factory(): def create_user(name, age=None, country='USA'): return {'name': name, 'age': age, 'country': country} return create_user correctly defines a fixture returning a function with default age=None and country='USA'. @pytest.fixture def user_factory(name, age=None, country='USA'): return {'name': name, 'age': age, 'country': country} is not a factory fixture because it takes parameters directly. @pytest.fixture def user_factory(): return {'name': 'default', 'age': None, 'country': 'USA'} returns a fixed dict, not a factory. @pytest.fixture def user_factory(): def create_user(name, age, country): return {'name': name, 'age': age, 'country': country} return create_user requires all parameters without defaults, so age and country are not optional.

3. Final Answer:

   @pytest.fixture def user_factory(): def create_user(name, age=None, country='USA'): return {'name': name, 'age': age, 'country': country} return create_user -> Option A

4. Quick Check:

   Factory fixture returns function with defaults [OK]

Hint: Factory fixture returns function with default parameters [OK]

Common Mistakes:

• Defining fixture with parameters directly
• Not providing default values for optional args
• Returning fixed data instead of a function