A data engineer investigates a slow stage in the Databricks Spark UI.

The metrics indicate data skew because one task is taking substantially longer than most tasks in the stage. Databricks advises checking the maximum task duration against the 75th-percentile duration: when the maximum is at least 50% higher, the stage may be skewed. Here, the 75th percentile is 50 seconds and the maximum is 80 seconds, so the maximum is 60% higher. The 25th percentile, median, and 75th percentile are tightly grouped between 40 and 50 seconds, while the maximum is a clear outlier. That pattern means a small number of tasks are processing disproportionately expensive partitions. These values alone do not prove cluster under-provisioning or excessive garbage collection; those diagnoses require resource, executor, spill, and GC metrics. Therefore, option A is correct.

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