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Which Spark version are you using in your project, and why did you choose it?

Spark/Big Dataeasy0.3 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Capgemini
Key Concepts Tested
pythonspark
Expert AnswerPremium
68 wordsInterview-ready
**Example**: Spark 3.4 for LTS, AQE improvements, Python 3.11. Choice factors: (1) Runtime (Databricks, EMR) compatibility. (2) Features (AQE, Delta). (3) Security. (4) Team familiarity. **Why It Matters**: Upgrades bring perf and fixes. Too old = no AQE, bugs. Too new = compatibility risk. **Scalability Trade-offs**: Pin versions. Test upgrades in staging. Vendor matrix. **Cost Implications**: Newer often faster; upgrade can reduce cost....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Capgemini. The answer also includes follow-up discussion points that interviewers commonly explore.

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