Your question is Optimize Spark for Performance and Cost. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you optimize a Spark job for performance and cost?
Discuss a practical approach covering diagnosis, partitioning, join strategy, shuffle reduction, file layout, caching, serialization, cluster sizing, adaptive query execution, and workload-specific trade-offs. Explain how you would validate improvements using Spark UI, execution metrics, data-quality checks, and cost monitoring.