Oct 08, 2026 by Offer

Python: decorators: Answer Guide 2027

A decorator is a function that wraps another function to add behavior — logging, timing, authentication — without changing the wrapped function's code. In a python decorators interview, explain the @ syntax as shorthand: @my_decorator above def f() is the same as f = my_decorator(f), and note decorators take a function and return a (usually enhanced) function.

What This Tests in a Python decorators interview Question

Whether you understand functions as first-class objects: passed in, returned, wrapped.
Whether you can explain the @ syntax as syntactic sugar rather than magic.
Whether you can name real uses: logging, timing, caching, retry logic, access control.

How to Answer a Python decorators interview Question

Define it: a decorator accepts a function and returns a wrapper that adds behavior around the call.
Demystify the syntax: @deco on def f() just rebinds f to deco(f).
Give two concrete uses and mention functools.wraps to preserve the original function's name and docstring.

Example phrasing: "A decorator wraps a function to add behavior without editing it — @timer on def process() is shorthand for process = timer(process). I use them for logging, timing, and retries, and I apply functools.wraps inside so the wrapped function keeps its name and docstring."

Common Mistakes in a Python decorators interview Question

Describing decorators as 'annotations' without explaining the wrapping mechanism.
Forgetting that the decorator runs at definition time, not at each call.
Not knowing functools.wraps, which interviewers often probe as the follow-up.

Python interviews love decorators because they test whether you understand functions as objects — a core language concept. A crisp 'wrapper plus @-syntax' explanation with one real use case signals genuine Python fluency, not tutorial-level familiarity.