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Describe how you would optimize slow-running Spark jobs in a distributed environment.

Spark/Big Datahard0.4 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
EPAM
Key Concepts Tested
optimizationpartitionspark
Expert AnswerPremium
74 wordsInterview-ready
**Why systematic optimization matters**: Ad-hoc fixes don't scale; methodical profiling does. **Approach**: (1) Profile—Spark UI: stages, tasks, shuffle read/write. (2) Partition pruning and predicate pushdown. (3) Broadcast small tables. (4) Repartition for skew. (5) Cache reused DataFrames. (6) Tune shuffle partitions. (7) Enable AQE. (8) Right-size executors. **Scalability trade-offs**: Each lever has limits; combine for best effect....
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 EPAM. The answer also includes follow-up discussion points that interviewers commonly explore.

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