Answer: Data skew resulting from uneven distribution, where certain Spark partitions contain significantly more records than others.

The scenario described—where most tasks (minimum and median) finish quickly but one or a few tasks (maximum) take orders of magnitude longer—is a classic symptom of **Data Skew**. ### Why this is correct: Data skew occurs when the underlying data is not distributed evenly across partitions. The tasks responsible for the 'heavy' partitions become **stragglers**, processing far more records than their peers and preventing the stage from completing. ### Why other options are incorrect: * **Network/Regional Latency:** If cross-region data transfer were the issue, you would likely see an increase in the median duration across many tasks, not just a single outlier. * **Task Queuing:** Thread pool issues would result in tasks waiting to start even when cores are available, affecting task scheduling rather than individual task execution duration. * **Disk Spillover:** While spills to disk slow down tasks, they usually occur when executor memory is exceeded and tend to affect multiple tasks or lead to Out-of-Memory (OOM) errors rather than creating a single task that is 100x slower.

Author: LeetQuiz Editorial Team