diff --git a/integration_tests/src/main/python/window_function_test.py b/integration_tests/src/main/python/window_function_test.py
index 9bbf93a6e26..02c78e296d4 100644
--- a/integration_tests/src/main/python/window_function_test.py
+++ b/integration_tests/src/main/python/window_function_test.py
@@ -213,3 +213,53 @@ def test_window_aggs_for_ranges_of_dates(data_gen):
         '       range between 1 preceding and 1 following) as sum_c_asc '
         'from window_agg_table'
     )
+
+
+'''
+  Spark will drop nulls when collecting, but seems GPU does not, so exceptions come up.
+E      Caused by: java.lang.AssertionError:  value at 350 is null
+E          	at ai.rapids.cudf.HostColumnVectorCore.assertsForGet(HostColumnVectorCore.java:228)
+E         	at ai.rapids.cudf.HostColumnVectorCore.getInt(HostColumnVectorCore.java:254)
+E         	at com.nvidia.spark.rapids.RapidsHostColumnVectorCore.getInt(RapidsHostColumnVectorCore.java:109)
+E         	at org.apache.spark.sql.vectorized.ColumnarArray.getInt(ColumnarArray.java:128)
+
+  Now set nullable to false to pass the tests, once native supports dropping nulls, will set it to true.
+'''
+collect_data_gen = [
+    ('a', RepeatSeqGen(LongGen(), length=20)),
+    ('b', IntegerGen()),
+    ('c_int', IntegerGen(nullable=False)),
+    ('c_long', LongGen(nullable=False)),
+    ('c_time', DateGen(nullable=False)),
+    ('c_string', StringGen(nullable=False)),
+    ('c_float', FloatGen(nullable=False)),
+    ('c_decimal', DecimalGen(nullable=False, precision=8, scale=3)),
+    ('c_struct', StructGen(nullable=False, children = [
+        ['child_int', IntegerGen()],
+        ['child_time', DateGen()],
+        ['child_string', StringGen()],
+        ['child_decimal', DecimalGen(nullable=False, precision=8, scale=3)]]))]
+
+# SortExec does not support array type, so sort the result locally.
+@ignore_order(local=True)
+@pytest.mark.parametrize('data_gen', [collect_data_gen], ids=idfn)
+def test_window_aggs_for_rows_collect_list(data_gen):
+    assert_gpu_and_cpu_are_equal_sql(
+        lambda spark : gen_df(spark, data_gen, length=2048),
+        "window_collect_table",
+        'select '
+        '  collect_list(c_int) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_int, '
+        '  collect_list(c_long) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_long, '
+        '  collect_list(c_time) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_time, '
+        '  collect_list(c_string) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_string, '
+        '  collect_list(c_float) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_float, '
+        '  collect_list(c_decimal) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_decimal, '
+        '  collect_list(c_struct) over '
+        '    (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_struct '
+        'from window_collect_table ')