Your question is Handle Spark Skew and Memory. 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 handle skew and memory bottlenecks when processing large distributed datasets using Apache Spark?
Explain how you would diagnose whether the problem is partition skew, insufficient executor memory, driver-side collection, or an inefficient transformation. Cover practical techniques such as Adaptive Query Execution, repartitioning, salting, broadcast joins, aggregation strategy, caching, and spill behavior. Keep the answer focused on Apache Spark internals and production troubleshooting rather than a specific business scenario.