Reviewed by Aditya Kumar · Last reviewed 2026-03-24
Python: merged = {**d1, **d2}; clean = {k: v for k, v in merged.items() if v is not None}. Or: def merge_no_nulls(*dicts): result = {}; [result.update({k: v for k, v in d.items() if v is not None}) for d in dicts]; return result. For nested: recursive merge. Production: use...
This easy-level SQL question appears frequently in data engineering interviews at companies like BCG. 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.
Python: merged = {d1, d2}; clean = {k: v for k, v in merged.items() if v is not None}. Or: def merge_no_nulls(*dicts): result = {}; [result.update({k: v for k, v in d.items() if v is not None}) for d in dicts]; return result. For nested: recursive merge. Production: use orjson for speed, validate schema. Example: d1={'a':1,'b':None}; d2={'b':2,'c':3}; {k:v for k,v in {d1,d2}.items() if v is not None} -> {'a':1,'b':2,'c':3}. Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.
Red Flag: Generic textbook answers. Pro-Move: 'At scale we measured X, implemented Y, achieved Z%—validated and iterated.'
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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.