Interview preparation
Python interview questions for Data Engineers
Every question starts with a direct answer you could say out loud, then explains why, when and what goes wrong. Tick questions off as you practise.
All Python questions
| # | Question | Difficulty | Type | Done |
|---|---|---|---|---|
| 1 | Explain shallow copy vs deep copy. | Easy | conceptual, debugging | |
| 2 | List vs tuple vs set: when would you use each in a data pipeline? | Easy | conceptual | |
| 3 | What is a generator and why can it help with large datasets? | Easy | conceptual, coding | |
| 4 | Why are mutable default arguments dangerous in Python? | Easy | conceptual, debugging | |
| 5 | How should exceptions be handled in production data pipelines? | Medium | conceptual, scenario | |
| 6 | How would you add retries with backoff to API ingestion in Python? | Medium | coding, scenario | |
| 7 | How would you process a 50 GB CSV file on a machine with 8 GB of RAM? | Medium | scenario, coding | |
| 8 | When would you not use pandas in a data pipeline? | Medium | conceptual, scenario | |
| 9 | Why are dict and set lookups O(1) in Python, and when are they not? | Medium | conceptual, optimization |
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Concept review
- LessonBeginnerPython Data Structures for Data Engineering Interviews
- LessonIntermediatePython Error Handling and Logging for Data Pipelines
- LessonBeginnerPython File I/O for Data Engineers: CSV, JSON, JSON Lines and Gzip
- LessonBeginnerPython Functions, Modules and Reusable Data Pipeline Code
- LessonIntermediateBuild an Idempotent CSV Loader in Python
- LessonIntermediatePython Iterators, Generators and Memory-Efficient Processing
- LessonIntermediatepandas for Data Engineers: Essentials and When Not to Use It
- LessonBeginnerPython for Data Engineering
- LessonIntermediateTesting Python Data Pipelines with pytest
- LessonIntermediateWorking with APIs in Python: Pagination, Retries and Rate Limits