*args: Variable positional args, received as tuple. **kwargs: Variable keyword args, received as dict. Why: Flexible signatures—wrappers, decorators, config-driven functions. Example: def run_pipeline(*sources, **config): ... allows run_pipeline('s3://a','s3://b',...
Red Flag: Only giving the tuple vs dict distinction. Pro-Move: 'We use **kwargs for connector config—each source has different options; one run(source, **connector_config) interface'—shows design 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.
args: Variable positional args, received as tuple. kwargs: Variable keyword args, received as dict. Why: Flexible signatures—wrappers, decorators, config-driven functions. Example: def run_pipeline(sources, config): ... allows run_pipeline('s3://a','s3://b', parallelism=4). Order: (regular, args, keyword-only, kwargs). Pitfall: Passing through—func(args, kwargs)—preserves interface. In data pipelines: kwargs for optional connector params (region, timeout, retries). Best practice: Document expected keys when using kwargs; validate in production code.
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According 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.