diff --git a/docs/sql-performance-tuning.md b/docs/sql-performance-tuning.md
index d595ba24c0..19799d95a7 100644
--- a/docs/sql-performance-tuning.md
+++ b/docs/sql-performance-tuning.md
@@ -234,7 +234,7 @@ The "REPARTITION_BY_RANGE" hint must have column names and a partition number is
 For more details please refer to the documentation of [Partitioning Hints](sql-ref-syntax-qry-select-hints.html#partitioning-hints).
 
 ## Adaptive Query Execution
-Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan. AQE is disabled by default. Spark SQL can use the umbrella configuration of `spark.sql.adaptive.enabled` to control whether turn it on/off. As of Spark 3.0, there are three major features in AQE, including coalescing post-shuffle partitions, converting sort-merge join to broadcast join, and skew join optimization.
+Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 3.2.0. Spark SQL can turn on and off AQE by `spark.sql.adaptive.enabled` as an umbrella configuration. As of Spark 3.0, there are three major features in AQE: including coalescing post-shuffle partitions, converting sort-merge join to broadcast join, and skew join optimization.
 
 ### Coalescing Post Shuffle Partitions
 This feature coalesces the post shuffle partitions based on the map output statistics when both `spark.sql.adaptive.enabled` and `spark.sql.adaptive.coalescePartitions.enabled` configurations are true. This feature simplifies the tuning of shuffle partition number when running queries. You do not need to set a proper shuffle partition number to fit your dataset. Spark can pick the proper shuffle partition number at runtime once you set a large enough initial number of shuffle partitions via `spark.sql.adaptive.coalescePartitions.initialPartitionNum` configuration.