Python Interview Questions

How do you structure a pytest automation framework?

Explain pytest fixtures and the difference between function, class, module, and session scope.

Fixtures are reusable setup/teardown functions injected into tests via dependency injection, and scope controls how often the fixture is created — function scope runs fresh per test, while session scope runs once for the entire test run.

How do you use the requests library to build API tests, including handling sessions and auth?

requests.Session() persists cookies, headers, and connection pooling across multiple calls, which is essential for API tests that need to authenticate once and reuse that auth state, rather than re-authenticating every request.

How do list and dict comprehensions improve readability when processing test data or API responses?

Comprehensions let you build a new list or dict from an iterable in a single readable expression, replacing multi-line for-loops with append/update calls, while also often running faster due to CPython optimizations.

Why do you use virtual environments, and how do you manage dependencies for a test automation project?

A virtualenv isolates a project's Python dependencies from the system's global packages, preventing version conflicts between projects and ensuring the exact library versions used in CI match what's on a developer's machine.

What are the key differences between unittest and pytest, and why might a team migrate from one to the other?

unittest is Python's built-in xUnit-style framework requiring class-based tests and self.assertX methods, while pytest allows plain functions with a simple assert statement, richer fixtures, and a much larger plugin ecosystem.

How do you use pytest.mark.parametrize to avoid duplicating near-identical test cases?

parametrize lets you run the same test function multiple times with different input/expected-output pairs supplied as a decorator, replacing several copy-pasted test functions with one data-driven test.

How would you structure a Page Object Model in Python for a Selenium/Playwright test suite?

Each page gets its own class encapsulating its locators and interaction methods (like login() or search()), so tests call high-level business actions instead of manipulating locators directly, isolating UI changes to one place.

What is data types, and why does it matter in Python?

data types is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply data types in a production Python project?

Apply data types through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is lists and tuples, and why does it matter in Python?

lists and tuples is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply lists and tuples in a production Python project?

Apply lists and tuples through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is dictionaries and sets, and why does it matter in Python?

dictionaries and sets is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply dictionaries and sets in a production Python project?

Apply dictionaries and sets through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is comprehensions, and why does it matter in Python?

comprehensions is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply comprehensions in a production Python project?

Apply comprehensions through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is functions, and why does it matter in Python?

functions is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply functions in a production Python project?

Apply functions through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is args and kwargs, and why does it matter in Python?

args and kwargs is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply args and kwargs in a production Python project?

Apply args and kwargs through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is decorators, and why does it matter in Python?

decorators is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply decorators in a production Python project?

Apply decorators through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is generators, and why does it matter in Python?

generators is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply generators in a production Python project?

Apply generators through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is iterators, and why does it matter in Python?

iterators is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply iterators in a production Python project?

Apply iterators through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is context managers, and why does it matter in Python?

context managers is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply context managers in a production Python project?

Apply context managers through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is exceptions, and why does it matter in Python?

exceptions is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply exceptions in a production Python project?

Apply exceptions through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is classes, and why does it matter in Python?

classes is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply classes in a production Python project?

Apply classes through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is inheritance, and why does it matter in Python?

inheritance is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply inheritance in a production Python project?

Apply inheritance through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is dataclasses, and why does it matter in Python?

dataclasses is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply dataclasses in a production Python project?

Apply dataclasses through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is type hints, and why does it matter in Python?

type hints is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply type hints in a production Python project?

Apply type hints through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is virtual environments, and why does it matter in Python?

virtual environments is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply virtual environments in a production Python project?

Apply virtual environments through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is packages and modules, and why does it matter in Python?

packages and modules is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply packages and modules in a production Python project?

Apply packages and modules through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is pytest fixtures, and why does it matter in Python?

pytest fixtures is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply pytest fixtures in a production Python project?

Apply pytest fixtures through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is pytest parametrize, and why does it matter in Python?

pytest parametrize is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply pytest parametrize in a production Python project?

Apply pytest parametrize through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is markers, and why does it matter in Python?

markers is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply markers in a production Python project?

Apply markers through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is monkeypatching, and why does it matter in Python?

monkeypatching is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply monkeypatching in a production Python project?

Apply monkeypatching through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is requests library, and why does it matter in Python?

requests library is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply requests library in a production Python project?

Apply requests library through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.

What is asyncio, and why does it matter in Python?

asyncio is a core Python concept used to make testing decisions more accurate, repeatable, and aligned with product risk.

How would you apply asyncio in a production Python project?

Apply asyncio through an explicit, repeatable design: define the expected behavior, automate the highest-risk path, capture useful diagnostics, and verify the result against a stable oracle.