Deterministic tests in PyTest - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a deterministic pytest function

What is the output of this pytest test function when run?

PyTest

import pytest

def add(a, b):
    return a + b

def test_add():
    assert add(2, 3) == 5
    assert add(0, 0) == 0
    assert add(-1, 1) == 0

A SyntaxError due to missing colon

B All assertions pass, test result: PASSED

C AssertionError on add(-1, 1) == 0, test result: FAILED

D TypeError because add expects strings

💡 Hint

Check the add function and the assertions carefully.

The add function correctly sums two numbers. All assertions match expected sums, so the test passes.

❓ assertion

intermediate

Choosing the correct assertion for deterministic output

Which assertion correctly tests that a function returns a fixed string 'hello' every time?

PyTest

def greet():
    return 'hello'

A assert greet() == 'Hello'

B assert greet() != 'hello'

C assert greet() == 'hello'

D assert greet() is 'hello'

💡 Hint

Check string equality and case sensitivity.

Option C correctly asserts the function returns the exact string 'hello'. Option C uses 'is' which is not reliable for string equality.

🔧 Debug

advanced

Identify why this pytest test is non-deterministic

Why does this test sometimes fail unpredictably?

PyTest

import random

def get_random_number():
    return random.randint(1, 10)

def test_random_number():
    assert get_random_number() == 5

A The test depends on random output, so it is non-deterministic and may fail.

B The function get_random_number always returns 5, so the test should pass.

C The test has a syntax error in the assert statement.

D The test is missing a pytest decorator.

💡 Hint

Think about what random.randint returns each time.

The function returns a random number between 1 and 10, so the assertion that it equals 5 is only sometimes true, causing flaky test failures.

❓ framework

advanced

Best practice to make a pytest test deterministic when testing time-dependent code

Which approach ensures a pytest test for a function that returns the current time is deterministic?

PyTest

import time

def get_current_time():
    return time.time()

A Mock time.time() to return a fixed timestamp during the test.

B Use assert get_current_time() > 0 to check it returns a positive number.

C Call get_current_time() twice and compare results.

D Add a sleep(1) before calling get_current_time() to stabilize output.

💡 Hint

Think about controlling external dependencies in tests.

Mocking time.time() fixes the returned value, making the test deterministic and repeatable.

🧠 Conceptual

expert

Why are deterministic tests important in continuous integration?

Choose the best reason why deterministic tests are critical in a CI pipeline.

A They require less code because they do not use assertions.

B They run faster than non-deterministic tests because they skip setup steps.

C They allow tests to randomly fail sometimes to catch hidden bugs.

D They ensure tests produce the same results every run, avoiding flaky failures that waste developer time.

💡 Hint

Think about reliability and trust in automated testing.

Deterministic tests give consistent results, so developers can trust test outcomes and fix real issues without chasing random failures.

Practice

1. What does it mean for a test to be deterministic in pytest?

easy

A. The test depends on external services to pass.

B. The test always produces the same result given the same inputs.

C. The test uses random data every time it runs.

D. The test runs faster than non-deterministic tests.

Solution

1. Step 1: Understand the meaning of deterministic tests

   Deterministic tests produce consistent results every time they run with the same inputs.

2. Step 2: Compare options with this definition

   Only The test always produces the same result given the same inputs. states that the test always produces the same result given the same inputs, matching the definition.

3. Final Answer:

   The test always produces the same result given the same inputs. -> Option B

4. Quick Check:

   Deterministic = Same result every time [OK]

Hint: Deterministic means repeatable results every run [OK]

Common Mistakes:

• Confusing deterministic with fast tests
• Thinking randomness is allowed in deterministic tests
• Assuming external dependencies make tests deterministic

2. Which of the following is the correct way to fix randomness in a pytest test to make it deterministic?

easy

A. Use a fixed seed for the random number generator.

B. Remove all assertions from the test.

C. Run the test multiple times and average the results.

D. Use random data without controlling it.

Solution

1. Step 1: Identify how to control randomness

   Setting a fixed seed for the random number generator ensures the same random values each run.

2. Step 2: Evaluate options for fixing randomness

   Only Use a fixed seed for the random number generator. uses a fixed seed, which makes the test deterministic. Other options do not control randomness properly.

3. Final Answer:

   Use a fixed seed for the random number generator. -> Option A

4. Quick Check:

   Fixed seed = deterministic randomness [OK]

Hint: Fix random seed to control randomness [OK]

Common Mistakes:

• Removing assertions does not fix randomness
• Averaging results does not make test deterministic
• Using uncontrolled random data causes flaky tests

3. Given this pytest test code, what will be the output when run twice?

import random

def test_random_number():
    random.seed(42)
    num = random.randint(1, 10)
    assert num == 2

medium

A. The test will pass both times because the seed fixes the random number.

B. The test will fail both times because 2 is not the generated number.

C. The test will pass the first time and fail the second time.

D. The test will fail the first time and pass the second time.

Solution

1. Step 1: Understand the effect of setting random.seed(42)

   Setting the seed to 42 makes random.randint(1, 10) produce the same number every run.

2. Step 2: Check the generated number and assertion

   With seed 42, random.randint(1, 10) returns 2, so the assertion num == 2 passes every time.

3. Final Answer:

   The test will pass both times because the seed fixes the random number. -> Option A

4. Quick Check:

   Seed 42 -> randint = 2 == 2 -> assert passes [OK]

Hint: Seed fixes random output to 2, so assertion num==2 passes consistently [OK]

Common Mistakes:

• Thinking the test passes because of seed (but checks wrong number)
• Assuming random changes every run despite seed
• Not verifying that seed(42) produces 2 for randint(1,10)

4. This pytest test sometimes fails randomly. What is the main issue?

import random

def test_random_fail():
    num = random.randint(1, 10)
    assert num == 5

medium

A. The test function name is invalid.

B. The assertion syntax is incorrect.

C. The test does not fix the random seed, causing non-deterministic results.

D. The random module is not imported.

Solution

1. Step 1: Identify randomness control in the test

   The test uses random.randint without setting a seed, so output varies each run.

2. Step 2: Understand why this causes test failure

   Because the number is random, the assertion num == 5 sometimes fails, making the test flaky.

3. Final Answer:

   The test does not fix the random seed, causing non-deterministic results. -> Option C

4. Quick Check:

   No seed -> random output varies -> flaky test [OK]

Hint: No seed means random output, causing flaky tests [OK]

Common Mistakes:

• Thinking assertion syntax is wrong
• Assuming test name affects determinism
• Ignoring that random module is correctly imported

5. You want to write a deterministic pytest test that checks if a function returns the current date. Which approach ensures determinism?

hard

A. Call the function without any changes and assert the result.

B. Run the test only once to avoid variability.

C. Use the actual current date and accept test failures on different days.

D. Mock the current date to a fixed value during the test.

Solution

1. Step 1: Understand why current date causes non-determinism

   The current date changes every day, so tests using it without control will fail on different days.

2. Step 2: Identify how to fix the date for deterministic testing

   Mocking the current date to a fixed value ensures the test always sees the same date and passes consistently.

3. Final Answer:

   Mock the current date to a fixed value during the test. -> Option D

4. Quick Check:

   Mock date -> fixed input -> deterministic test [OK]

Hint: Mock changing data like dates for stable tests [OK]

Common Mistakes:

• Using real current date causes flaky tests
• Ignoring mocking leads to non-deterministic results
• Running test once does not guarantee determinism