In a real project, tell me about a time you had to troubleshoot an EMR issue that affected performance. What specific steps did you take to isolate the root cause?

S: In a data pipeline project, our EMR cluster started showing 4x slower job runtimes during peak hours. T: I was tasked with diagnosing the slowdown. A: I first examined the Spark UI to identify stages with high shuffle times and looked for tasks with long execution times. I noticed a single key causing data skew, leading to one task taking 30 minutes while others finished quickly. I checked the driver logs for OOM errors and confirmed the skew. I then applied a salting technique to the skewed key, increased the number of executors, and tuned the shuffle partitions. After re-running the job, the runtime dropped to normal levels. R: The root cause was data skew, and the corrective actions improved performance by 70%, ensuring SLA compliance.