diff --git a/docs/sql-migration-guide.md b/docs/sql-migration-guide.md
index 8f6a415569863..4707e491fa674 100644
--- a/docs/sql-migration-guide.md
+++ b/docs/sql-migration-guide.md
@@ -97,6 +97,7 @@ license: |
   - Since Spark 3.4, `BinaryType` is not supported in CSV datasource. In Spark 3.3 or earlier, users can write binary columns in CSV datasource, but the output content in CSV files is `Object.toString()` which is meaningless; meanwhile, if users read CSV tables with binary columns, Spark will throw an `Unsupported type: binary` exception.
   - Since Spark 3.4, bloom filter joins are enabled by default. To restore the legacy behavior, set `spark.sql.optimizer.runtime.bloomFilter.enabled` to `false`.
   - Since Spark 3.4, when schema inference on external Parquet files, INT64 timestamps with annotation `isAdjustedToUTC=false` will be inferred as TimestampNTZ type instead of Timestamp type. To restore the legacy behavior, set `spark.sql.parquet.inferTimestampNTZ.enabled` to `false`.
+  - Since Spark 3.4, the behavior for `CREATE TABLE AS SELECT ...` is changed from OVERWRITE to APPEND when `spark.sql.legacy.allowNonEmptyLocationInCTAS` is set to `true`. Users are recommended to avoid CTAS with a non-empty table location.
 
 ## Upgrading from Spark SQL 3.2 to 3.3