**Decorators** are higher-order functions that wrap another function to add or change behavior. Syntax: `@decorator` before a function. Under the hood: `@deco` is equivalent to `func = deco(func)`. **Why it matters**: Cross-cutting concerns (logging, timing, retries, auth)...
Red Flag: Decorator that doesn't use functools.wraps—breaks introspection and debugging. Pro-Move: 'I use decorators for retry, logging, and timing; I always use @wraps and parameterized decorators for config (e.g., retry(max_attempts=3)).'
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Altimetrik, Infosys. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (python) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Decorators are higher-order functions that wrap another function to add or change behavior. Syntax: @decorator before a function. Under the hood: @deco is equivalent to func = deco(func). Why it matters: Cross-cutting concerns (logging, timing, retries, auth) without modifying core logic. Architectural benefit: Separation of concerns; reusable wrappers. Example: def retry(n):\n def wrap(f):\n def inner(a,k): ... return f(a,k)\n return inner\n return wrap. Scalability/cost: Decorators add a thin call layer; use functools.wraps to preserve metadata. Avoid heavy logic in decorators for hot paths.
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Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 2 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.