diff --git a/_questions/data-engineering-zoomcamp/module-6/066_6f04719ff8_spark-max-trip-distance-outliers-filter.md b/_questions/data-engineering-zoomcamp/module-6/066_6f04719ff8_spark-max-trip-distance-outliers-filter.md
new file mode 100644
index 00000000..3041626f
--- /dev/null
+++ b/_questions/data-engineering-zoomcamp/module-6/066_6f04719ff8_spark-max-trip-distance-outliers-filter.md
@@ -0,0 +1,18 @@
+---
+id: 6f04719ff8
+question: Why does max(trip_distance) return extremely large values in Spark for yellow_tripdata_2023-11,
+  and how can I obtain a realistic maximum?
+sort_order: 66
+---
+
+- This issue is caused by data quality problems in the NYC Taxi dataset. Some rows have unrealistic trip_distance values due to GPS errors, sensor faults, corrupted trip records, or incorrect meter readings. When Spark computes max(trip_distance) without filtering, these outliers inflate the result.
+
+- To obtain a more realistic maximum, apply a simple filter before computing the maximum. For example:
+
+```python
+df.filter('trip_distance > 0 AND trip_distance < 200').selectExpr('max(trip_distance)').show()
+```
+
+- Notes:
+  - The threshold (200 in the example) is dataset-specific; adjust it to reflect plausible trip distances for your data.
+  - This approach helps you reflect typical taxi trips rather than including extreme outliers.