**Essential Properties**: `--master` (yarn, local, k8s), `--deploy-mode` (client/cluster), `--driver-memory`, `--executor-memory`, `--num-executors`, `--executor-cores`. `--conf` for fine-grained: `spark.sql.shuffle.partitions`, `spark.dynamicAllocation.enabled`.
**Why They Matter**: Wrong memory = OOM or waste. Too few executors = underutilization. Too many = overhead....
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 HCL. The answer also includes follow-up discussion points that interviewers commonly explore.
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