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Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.

Spark/Big Dataeasy0.3 min readPremium
Frequency
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Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
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1,863
across 7 categories
Asked at these companies
Capgemini
Key Concepts Tested
spark
Expert AnswerPremium
56 wordsInterview-ready
**Why config matters**: Right-sizing = cost and performance. **Typical**: Driver 4–8GB, 2–4 cores. Executors: 8–16GB, 4–8 cores each. `spark.executor.memory`, `spark.executor.cores`. Dynamic allocation. YARN memory overhead ~10%. **Scalability trade-offs**: 4–5 tasks per core; too many small executors = overhead. **Cost implications**: Over-provision = waste; under = OOM....
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