Testing with external services in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pytest test with mocked external API call

What is the output of this pytest test when the external API call is mocked to return status code 200?

PyTest

import requests
import pytest
from unittest.mock import patch

def fetch_data():
    response = requests.get('https://api.example.com/data')
    if response.status_code == 200:
        return response.json()
    return None

@patch('requests.get')
def test_fetch_data(mock_get):
    mock_get.return_value.status_code = 200
    mock_get.return_value.json = lambda: {'key': 'value'}
    result = fetch_data()
    assert result == {'key': 'value'}
    print('Test passed')

A Test passed

B TypeError

C AssertionError

D No output

💡 Hint

Consider what the mock object returns for status_code and json method.

The mock replaces requests.get to return a response with status_code 200 and a json method returning {'key': 'value'}. The assertion passes and 'Test passed' is printed.

❓ assertion

intermediate

Correct assertion for API response content

Which assertion correctly verifies that the API response contains a 'user' key with a non-empty dictionary?

PyTest

response = {'user': {'id': 1, 'name': 'Alice'}}

A assert 'user' in response and isinstance(response['user'], dict) and response['user']

B assert response['user'] != None

C assert response['user'] is not None and len(response['user']) > 0

D assert response.get('user') == {}

💡 Hint

Check for key existence, type, and non-empty dictionary.

Option A checks that 'user' key exists, is a dictionary, and is not empty. Option A only checks not None but could be empty. Option A is valid but less explicit about type. Option A asserts empty dict which is incorrect.

🔧 Debug

advanced

Identify the cause of test failure with external service timeout

Given this pytest test, why does it fail with a TimeoutError?

PyTest

import requests
import pytest

def get_data():
    return requests.get('https://slow.api.example.com/data', timeout=1).json()

def test_get_data():
    data = get_data()
    assert 'result' in data

A The requests.get call is missing required headers causing a connection error

B The external API is too slow and exceeds the 1 second timeout causing TimeoutError

C The test assertion is incorrect and causes AssertionError

D The function get_data returns None causing TypeError

💡 Hint

Consider the timeout parameter in requests.get and the error type.

The test fails because the external API response takes longer than 1 second, triggering a TimeoutError before the assertion.

❓ framework

advanced

Best pytest fixture scope for external service setup

Which pytest fixture scope is best to minimize external service setup overhead when running multiple tests that share the same service state?

A module - runs setup once per module

B function - runs setup before each test function

C session - runs setup once per test session

D class - runs setup once per test class

💡 Hint

Think about how often you want to initialize the external service for all tests.

Session scope runs once for the entire test session, minimizing repeated setup for external services shared across tests.

🧠 Conceptual

expert

Handling flaky tests caused by external service instability

Which strategy is most effective to handle flaky tests caused by intermittent failures of an external service?

A Run the tests only manually when needed

B Ignore the flaky tests to avoid blocking the pipeline

C Increase the test timeout indefinitely to wait for the service

D Use test retries with exponential backoff and mock the external service in CI

💡 Hint

Consider reliability and automation best practices.

Retries with backoff help handle transient failures, and mocking in CI ensures tests are stable and fast. Ignoring or manual runs reduce test coverage and automation benefits.

Practice

1. What is the main reason to use mocking when testing with external services in pytest?

easy

A. To avoid calling the real external service and make tests faster

B. To increase the number of real API calls during testing

C. To make tests dependent on internet speed

D. To test the external service itself

Solution

1. Step 1: Understand the role of mocking in tests

   Mocking replaces real external calls with fake ones to avoid delays and failures.

2. Step 2: Identify the benefit of mocking external services

   Mocking makes tests faster and more reliable by not depending on real services.

3. Final Answer:

   To avoid calling the real external service and make tests faster -> Option A

4. Quick Check:

   Mocking speeds up tests by faking external calls [OK]

Hint: Mock external calls to speed tests and avoid failures [OK]

Common Mistakes:

• Thinking mocking increases real API calls
• Believing tests should depend on internet speed
• Confusing testing external service with testing your code

2. Which of the following is the correct way to mock a function get_data from module external_api using pytest's patch decorator?

easy

A. @patch('external_api.get_data')

B. @patch('get_data.external_api')

C. @patch('external_api->get_data')

D. @patch('external_api.getData')

Solution

1. Step 1: Recall correct patch syntax

   The patch decorator requires the full import path as a string: 'module.function'.

2. Step 2: Match the correct option

   @patch('external_api.get_data') uses 'external_api.get_data' which is the correct format and case-sensitive.

3. Final Answer:

   @patch('external_api.get_data') -> Option A

4. Quick Check:

   patch('module.function') syntax is correct [OK]

Hint: Use 'module.function' string in patch decorator [OK]

Common Mistakes:

• Swapping module and function order
• Using wrong separators like '->'
• Incorrect function name casing

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

from unittest.mock import patch
import requests

def fetch_status():
    response = requests.get('https://api.example.com/data')
    return response.status_code

@patch('requests.get')
def test_fetch_status(mock_get):
    mock_get.return_value.status_code = 200
    assert fetch_status() == 200
    print('Test passed')

medium

A. TypeError

B. AssertionError

C. Test passed

D. No output

Solution

1. Step 1: Understand mocking effect on requests.get

   The patch replaces requests.get with a mock that returns an object with status_code 200.

2. Step 2: Check assertion and print statement

   fetch_status() returns 200, matching the assertion, so 'Test passed' is printed.

3. Final Answer:

   Test passed -> Option C

4. Quick Check:

   Mocked return_value.status_code = 200 makes test pass [OK]

Hint: Mock return_value to control function output [OK]

Common Mistakes:

• Forgetting to set return_value.status_code
• Expecting real HTTP call instead of mock
• Missing print output due to assertion failure

4. Identify the error in the following pytest test that mocks an external service call:

from unittest.mock import patch
import myservice

@patch('myservice.call_api')
def test_api(mock_call):
    mock_call.return_value = {'status': 'ok'}
    result = myservice.call_api()
    assert result.status == 'ok'

medium

A. The patch decorator is missing parentheses

B. The assertion should use result['status'] instead of result.status

C. mock_call.return_value should be a string, not a dict

D. The test function is missing a return statement

Solution

1. Step 1: Analyze the mocked return value type

   The mock returns a dictionary {'status': 'ok'}, so result is a dict.

2. Step 2: Check the assertion syntax

   Accessing dict keys requires bracket notation, not dot notation; result.status causes AttributeError.

3. Final Answer:

   The assertion should use result['status'] instead of result.status -> Option B

4. Quick Check:

   Dict keys need brackets, not dot notation [OK]

Hint: Use brackets for dict keys in assertions [OK]

Common Mistakes:

• Using dot notation on dicts
• Forgetting parentheses in patch decorator (not here)
• Expecting return_value must be string always

5. You want to test a function process_data() that calls an external API fetch_data(). The API sometimes returns null. How should you mock fetch_data in pytest to test process_data handles null correctly?

hard

A. Do not mock fetch_data; test process_data only with real data

B. Call the real fetch_data to see if it returns null

C. Mock fetch_data to always raise an exception

D. Use patch to make fetch_data return null and assert process_data handles it

Solution

1. Step 1: Understand the need to test null handling

   Since fetch_data can return null, tests must simulate this to check process_data behavior.

2. Step 2: Use patch to mock fetch_data returning null

   Mocking fetch_data to return null allows controlled testing of process_data's handling of that case.

3. Final Answer:

   Use patch to make fetch_data return null and assert process_data handles it -> Option D

4. Quick Check:

   Mock null return to test edge case handling [OK]

Hint: Mock edge cases like null to test error handling [OK]

Common Mistakes:

• Calling real external API in tests
• Mocking only success cases, ignoring null
• Ignoring exceptions instead of testing them