diff --git a/datafusion/spark/src/function/array/repeat.rs b/datafusion/spark/src/function/array/repeat.rs
index ea9758ee9891d..c4e5e449c7535 100644
--- a/datafusion/spark/src/function/array/repeat.rs
+++ b/datafusion/spark/src/function/array/repeat.rs
@@ -28,7 +28,7 @@ use crate::function::null_utils::{
     NullMaskResolution, apply_null_mask, compute_null_mask,
 };
 
-/// Spark-compatible `array_repeat` expression. The difference with DataFusion's `array_repeat` is the handling of NULL inputs: in spark if any input is NULL, the result is NULL.
+/// Spark-compatible `array_repeat` expression. The difference with DataFusion's `array_repeat` is the handling of NULL count: in Spark if the count is NULL, the result is NULL.
 /// <https://spark.apache.org/docs/latest/api/sql/index.html#array_repeat>
 #[derive(Debug, PartialEq, Eq, Hash)]
 pub struct SparkArrayRepeat {
@@ -88,7 +88,7 @@ impl ScalarUDFImpl for SparkArrayRepeat {
 }
 
 /// This is a Spark-specific wrapper around DataFusion's array_repeat that returns NULL
-/// if any argument is NULL (Spark behavior), whereas DataFusion's array_repeat ignores NULLs.
+/// if the count argument is NULL (Spark behavior), whereas DataFusion's array_repeat ignores NULLs.
 fn spark_array_repeat(args: ScalarFunctionArgs) -> Result<ColumnarValue> {
     let ScalarFunctionArgs {
         args: arg_values,
@@ -99,15 +99,14 @@ fn spark_array_repeat(args: ScalarFunctionArgs) -> Result<ColumnarValue> {
     } = args;
     let return_type = return_field.data_type().clone();
 
-    // Step 1: Check for NULL mask in incoming args
-    let null_mask = compute_null_mask(&arg_values, number_rows)?;
+    // A NULL element should be repeated into the array, not cause a NULL result.
+    let null_mask = compute_null_mask(&arg_values[1..], number_rows)?;
 
-    // If any argument is null then return NULL immediately
+    // If count is null then return NULL immediately
     if matches!(null_mask, NullMaskResolution::ReturnNull) {
         return Ok(ColumnarValue::Scalar(ScalarValue::try_from(return_type)?));
     }
 
-    // Step 2: Delegate to DataFusion's array_repeat
     let array_repeat_func = ArrayRepeat::new();
     let func_args = ScalarFunctionArgs {
         args: arg_values,
@@ -118,6 +117,5 @@ fn spark_array_repeat(args: ScalarFunctionArgs) -> Result<ColumnarValue> {
     };
     let result = array_repeat_func.invoke_with_args(func_args)?;
 
-    // Step 3: Apply NULL mask to result
     apply_null_mask(result, null_mask, &return_type)
 }
diff --git a/datafusion/sqllogictest/test_files/spark/array/array_repeat.slt b/datafusion/sqllogictest/test_files/spark/array/array_repeat.slt
index 19181aae0fc55..923e349140976 100644
--- a/datafusion/sqllogictest/test_files/spark/array/array_repeat.slt
+++ b/datafusion/sqllogictest/test_files/spark/array/array_repeat.slt
@@ -59,7 +59,17 @@ SELECT array_repeat(['123'], 2);
 query ?
 SELECT array_repeat(NULL, 2);
 ----
-NULL
+[NULL, NULL]
+
+query ?
+SELECT array_repeat(NULL, 1);
+----
+[NULL]
+
+query ?
+SELECT array_repeat(NULL, 0);
+----
+[]
 
 query ?
 SELECT array_repeat([NULL], 2);
@@ -88,7 +98,7 @@ FROM VALUES
 [123, 123]
 []
 []
-NULL
+[NULL]
 NULL