Decorator: Higher-order function that wraps another function; @decorator syntax. Implementation: def timer(f): def wrapper(*args, **kwargs): start=time(); r=f(*args,**kwargs); print(time()-start); return r; return wrapper. Why: Cross-cutting concerns—logging, retries, auth,...
Red Flag: Defining decorator syntax without showing the underlying higher-order function. Pro-Move: 'We use @retry(max_attempts=3, backoff=2) on our S3 fetchers—centralized retry logic, easy to tune'—shows pipeline application.
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Delivery Hero, Fragma Data Systems, Swiggy. 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.
Decorator: Higher-order function that wraps another function; @decorator syntax. Implementation: def timer(f): def wrapper(args, kwargs): start=time(); r=f(args,kwargs); print(time()-start); return r; return wrapper. Why: Cross-cutting concerns—logging, retries, auth, caching—without cluttering business logic. In data pipelines: @retry, @logged, @rate_limited on API fetchers. Scalability: Decorators add indirection; overuse can make stack traces deep. Best practice: Use functools.wraps to preserve metadata; consider class-based decorators for stateful behavior.
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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 3 companies. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.