List: Mutable, []; tuple: immutable, (). Why it matters: Mutability drives use—lists for collections that change; tuples for fixed data, dict keys (hashable), multiple return values. Performance: Tuples are slightly faster (less overhead, fixed size). Hashability: Tuples can be...
Red Flag: Only stating 'list mutable, tuple immutable' without use cases. Pro-Move: 'We use tuples for (partition_key, path) in our DAG—hashable for dedup—and lists for accumulating rows before batch write'—connects to data engineering.
This easy-level Python/Coding question appears frequently in data engineering interviews at companies like Accenture, Delivery Hero, Fragma Data Systems. 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.
List: Mutable, []; tuple: immutable, (). Why it matters: Mutability drives use—lists for collections that change; tuples for fixed data, dict keys (hashable), multiple return values. Performance: Tuples are slightly faster (less overhead, fixed size). Hashability: Tuples can be dict keys/set members; lists cannot. In data pipelines: Tuples for schema-like rows (column names); lists for buffers, accumulators. Best practice: Use tuples for fixed-size records; lists when you need append/remove.
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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.