diff --git a/docs/supported_ops.md b/docs/supported_ops.md
index 645162b647d..0d33620ecb3 100644
--- a/docs/supported_ops.md
+++ b/docs/supported_ops.md
@@ -15459,9 +15459,9 @@ are limited.
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
+<td><em>PS<br/>Support for arrays of arrays of floats/doubles requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>Support for structs containing float/double array columns requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -15480,7 +15480,7 @@ are limited.
 <td> </td>
 <td> </td>
 <td> </td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td> </td>
 <td> </td>
 <td> </td>
@@ -15502,9 +15502,9 @@ are limited.
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
+<td><em>PS<br/>Support for arrays of arrays of floats/doubles requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>Support for structs containing float/double array columns requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -15523,7 +15523,7 @@ are limited.
 <td> </td>
 <td> </td>
 <td> </td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td> </td>
 <td> </td>
 <td> </td>
@@ -15545,9 +15545,9 @@ are limited.
 <td>S</td>
 <td><b>NS</b></td>
 <td><b>NS</b></td>
+<td><em>PS<br/>Support for arrays of arrays of floats/doubles requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
-<td><b>NS</b></td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>Support for structs containing float/double array columns requires spark.rapids.sql.hasNans to be set to false;<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td><b>NS</b></td>
 </tr>
 <tr>
@@ -15566,7 +15566,7 @@ are limited.
 <td> </td>
 <td> </td>
 <td> </td>
-<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, ARRAY, MAP, UDT</em></td>
+<td><em>PS<br/>UTC is only supported TZ for child TIMESTAMP;<br/>unsupported child types BINARY, CALENDAR, MAP, UDT</em></td>
 <td> </td>
 <td> </td>
 <td> </td>
diff --git a/integration_tests/src/main/python/data_gen.py b/integration_tests/src/main/python/data_gen.py
index e9057b77d6b..2e41927d14b 100644
--- a/integration_tests/src/main/python/data_gen.py
+++ b/integration_tests/src/main/python/data_gen.py
@@ -949,6 +949,10 @@ def gen_scalars_for_sql(data_gen, count, seed=0, force_no_nulls=False):
 # all of the basic types in a single struct
 all_basic_struct_gen = StructGen([['child'+str(ind), sub_gen] for ind, sub_gen in enumerate(all_basic_gens)])
 
+all_basic_struct_gen_no_nan = StructGen([['child'+str(ind), sub_gen] for ind, sub_gen in enumerate(all_basic_gens_no_nan)])
+
+struct_array_gen_no_nans =  StructGen([['child'+str(ind), sub_gen] for ind, sub_gen in enumerate(single_level_array_gens_no_nan)])
+
 # Some struct gens, but not all because of nesting
 nonempty_struct_gens_sample = [all_basic_struct_gen,
         StructGen([['child0', byte_gen], ['child1', all_basic_struct_gen]]),
diff --git a/integration_tests/src/main/python/hash_aggregate_test.py b/integration_tests/src/main/python/hash_aggregate_test.py
index 43948645574..09e4abd89a5 100644
--- a/integration_tests/src/main/python/hash_aggregate_test.py
+++ b/integration_tests/src/main/python/hash_aggregate_test.py
@@ -574,10 +574,23 @@ def test_hash_pivot_groupby_duplicates_fallback(data_gen):
     RepeatSeqGen(StructGen([
         ['c0', all_basic_struct_gen], ['c1', int_gen]]), length=15)]
 
+# data generating for collect_set based-nested Struct[Array] types
+_repeat_agg_column_for_collect_set_op_nested = [
+    RepeatSeqGen(struct_array_gen_no_nans, length=15),
+    RepeatSeqGen(StructGen([
+        ['c0', struct_array_gen_no_nans], ['c1', int_gen]]), length=15),
+    RepeatSeqGen(ArrayGen(all_basic_struct_gen_no_nan), length=15)]
+
+_array_of_array_gen = [RepeatSeqGen(ArrayGen(sub_gen), length=15) for sub_gen in single_level_array_gens_no_nan]
+
 _gen_data_for_collect_set_op = [[
     ('a', RepeatSeqGen(LongGen(), length=20)),
     ('b', value_gen)] for value_gen in _repeat_agg_column_for_collect_set_op]
 
+_gen_data_for_collect_set_op_nested = [[
+    ('a', RepeatSeqGen(LongGen(), length=20)),
+    ('b', value_gen)] for value_gen in _repeat_agg_column_for_collect_set_op_nested + _array_of_array_gen]
+
 # very simple test for just a count on decimals 128 values until we can support more with them
 @ignore_order(local=True)
 @pytest.mark.parametrize('data_gen', [decimal_gen_128bit], ids=idfn)
@@ -622,9 +635,7 @@ def test_hash_groupby_collect_list(data_gen, use_obj_hash_agg):
         conf={'spark.sql.execution.useObjectHashAggregateExec': str(use_obj_hash_agg).lower(),
             'spark.sql.shuffle.partitions': '1'})
 
-@approximate_float
 @ignore_order(local=True)
-@incompat
 @pytest.mark.parametrize('data_gen', _full_gen_data_for_collect_op, ids=idfn)
 def test_hash_groupby_collect_set(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
@@ -632,9 +643,7 @@ def test_hash_groupby_collect_set(data_gen):
             .groupby('a')
             .agg(f.sort_array(f.collect_set('b')), f.count('b')))
 
-@approximate_float
 @ignore_order(local=True)
-@incompat
 @pytest.mark.parametrize('data_gen', _gen_data_for_collect_set_op, ids=idfn)
 def test_hash_groupby_collect_set_on_nested_type(data_gen):
     assert_gpu_and_cpu_are_equal_collect(
@@ -642,9 +651,75 @@ def test_hash_groupby_collect_set_on_nested_type(data_gen):
             .groupby('a')
             .agg(f.sort_array(f.collect_set('b'))))
 
-@approximate_float
+
+# Note, using sort_array() on the CPU, because sort_array() does not yet
+# support sorting certain nested/arbitrary types on the GPU
+# See https://github.com/NVIDIA/spark-rapids/issues/3715
+# and https://github.com/rapidsai/cudf/issues/11222
+@ignore_order(local=True)
+@allow_non_gpu("ProjectExec", "SortArray")
+@pytest.mark.parametrize('data_gen', _gen_data_for_collect_set_op_nested, ids=idfn)
+def test_hash_groupby_collect_set_on_nested_array_type(data_gen):
+    conf = copy_and_update(_no_nans_float_conf, {
+        "spark.rapids.sql.castFloatToString.enabled": "true",
+        "spark.rapids.sql.castDecimalToString.enabled": "true",
+        "spark.rapids.sql.expression.SortArray": "false"
+    })
+
+    def do_it(spark):
+        df = gen_df(spark, data_gen, length=100)\
+            .groupby('a')\
+            .agg(f.collect_set('b').alias("collect_set"))
+        # pull out the rdd and schema and create a new dataframe to run SortArray
+        # to handle Spark 3.3.0+ optimization that moves SortArray from ProjectExec
+        # to ObjectHashAggregateExec
+        return spark.createDataFrame(df.rdd, schema=df.schema)\
+            .selectExpr("sort_array(collect_set)")
+        
+    assert_gpu_and_cpu_are_equal_collect(do_it, conf=conf)
+
+
+@ignore_order(local=True)
+@pytest.mark.parametrize('data_gen', _full_gen_data_for_collect_op, ids=idfn)
+def test_hash_reduction_collect_set(data_gen):
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: gen_df(spark, data_gen, length=100)
+            .agg(f.sort_array(f.collect_set('b')), f.count('b')))
+
+@ignore_order(local=True)
+@pytest.mark.parametrize('data_gen', _gen_data_for_collect_set_op, ids=idfn)
+def test_hash_reduction_collect_set_on_nested_type(data_gen):
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: gen_df(spark, data_gen, length=100)
+            .agg(f.sort_array(f.collect_set('b'))))
+
+
+# Note, using sort_array() on the CPU, because sort_array() does not yet
+# support sorting certain nested/arbitrary types on the GPU
+# See https://github.com/NVIDIA/spark-rapids/issues/3715
+# and https://github.com/rapidsai/cudf/issues/11222
+@ignore_order(local=True)
+@allow_non_gpu("ProjectExec", "SortArray")
+@pytest.mark.parametrize('data_gen', _gen_data_for_collect_set_op_nested, ids=idfn)
+def test_hash_reduction_collect_set_on_nested_array_type(data_gen):
+    conf = copy_and_update(_no_nans_float_conf, {
+        "spark.rapids.sql.castFloatToString.enabled": "true",
+        "spark.rapids.sql.castDecimalToString.enabled": "true",
+        "spark.rapids.sql.expression.SortArray": "false"
+    })
+
+    def do_it(spark):
+        df = gen_df(spark, data_gen, length=100)\
+            .agg(f.collect_set('b').alias("collect_set"))
+        # pull out the rdd and schema and create a new dataframe to run SortArray
+        # to handle Spark 3.3.0+ optimization that moves SortArray from ProjectExec
+        # to ObjectHashAggregateExec
+        return spark.createDataFrame(df.rdd, schema=df.schema)\
+            .selectExpr("sort_array(collect_set)")
+        
+    assert_gpu_and_cpu_are_equal_collect(do_it, conf=conf)
+
 @ignore_order(local=True)
-@incompat
 @pytest.mark.parametrize('data_gen', _full_gen_data_for_collect_op, ids=idfn)
 def test_hash_groupby_collect_with_single_distinct(data_gen):
     # test collect_ops with other distinct aggregations
@@ -656,9 +731,7 @@ def test_hash_groupby_collect_with_single_distinct(data_gen):
                  f.countDistinct('c'),
                  f.count('c')))
 
-@approximate_float
 @ignore_order(local=True)
-@incompat
 @pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
 def test_hash_groupby_single_distinct_collect(data_gen):
     # test distinct collect
@@ -681,7 +754,6 @@ def test_hash_groupby_single_distinct_collect(data_gen):
         df_fun=lambda spark: gen_df(spark, data_gen, length=100),
         table_name="tbl", sql=sql)
 
-@approximate_float
 @ignore_order(local=True)
 @pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
 def test_hash_groupby_collect_with_multi_distinct(data_gen):
diff --git a/integration_tests/src/main/python/window_function_test.py b/integration_tests/src/main/python/window_function_test.py
index 313e4f57b66..b18fa095c5c 100644
--- a/integration_tests/src/main/python/window_function_test.py
+++ b/integration_tests/src/main/python/window_function_test.py
@@ -139,6 +139,11 @@
         FloatGen(no_nans=True, special_cases=[]), DoubleGen(no_nans=True, special_cases=[]),
         string_gen, boolean_gen, date_gen, timestamp_gen, null_gen]
 
+_no_nans_float_conf = {'spark.rapids.sql.variableFloatAgg.enabled': 'true',
+                       'spark.rapids.sql.hasNans': 'false',
+                       'spark.rapids.sql.castStringToFloat.enabled': 'true'
+                      }
+
 @ignore_order
 @pytest.mark.parametrize('data_gen', [decimal_gen_128bit], ids=idfn)
 def test_decimal128_count_window(data_gen):
@@ -920,6 +925,28 @@ def test_running_window_function_exec_for_all_aggs():
     ('c_fp_nan', RepeatSeqGen(FloatGen().with_special_case(math.nan, 200.0), length=5)),
 ]
 
+_gen_data_for_collect_set_nested = [
+    ('a', RepeatSeqGen(LongGen(), length=20)),
+    ('b', LongRangeGen()),
+    ('c_int', RepeatSeqGen(IntegerGen(), length=15)),
+    ('c_struct_array_1', RepeatSeqGen(struct_array_gen_no_nans, length=15)),
+    ('c_struct_array_2', RepeatSeqGen(StructGen([
+        ['c0', struct_array_gen_no_nans], ['c1', int_gen]]), length=14)),
+    ('c_array_struct', RepeatSeqGen(ArrayGen(all_basic_struct_gen_no_nan), length=15)),
+    ('c_array_array_bool', RepeatSeqGen(ArrayGen(ArrayGen(BooleanGen())), length=15)),
+    ('c_array_array_int', RepeatSeqGen(ArrayGen(ArrayGen(IntegerGen())), length=15)),
+    ('c_array_array_long', RepeatSeqGen(ArrayGen(ArrayGen(LongGen())), length=15)),
+    ('c_array_array_short', RepeatSeqGen(ArrayGen(ArrayGen(ShortGen())), length=15)),
+    ('c_array_array_date', RepeatSeqGen(ArrayGen(ArrayGen(DateGen())), length=15)),
+    ('c_array_array_timestamp', RepeatSeqGen(ArrayGen(ArrayGen(TimestampGen())), length=15)),
+    ('c_array_array_byte', RepeatSeqGen(ArrayGen(ArrayGen(ByteGen())), length=15)),
+    ('c_array_array_string', RepeatSeqGen(ArrayGen(ArrayGen(StringGen())), length=15)),
+    ('c_array_array_float', RepeatSeqGen(ArrayGen(ArrayGen(FloatGen(no_nans=True))), length=15)),
+    ('c_array_array_double', RepeatSeqGen(ArrayGen(ArrayGen(DoubleGen(no_nans=True))), length=15)),
+    ('c_array_array_decimal_32', RepeatSeqGen(ArrayGen(ArrayGen(DecimalGen(precision=8, scale=3))), length=15)),
+    ('c_array_array_decimal_64', RepeatSeqGen(ArrayGen(ArrayGen(decimal_gen_64bit)), length=15)),
+    ('c_array_array_decimal_128', RepeatSeqGen(ArrayGen(ArrayGen(decimal_gen_128bit)), length=15)),
+]
 
 # SortExec does not support array type, so sort the result locally.
 @ignore_order(local=True)
@@ -977,6 +1004,86 @@ def test_window_aggs_for_rows_collect_set():
         ) t
         ''')
 
+
+# Note, using sort_array() on the CPU, because sort_array() does not yet
+# support sorting certain nested/arbitrary types on the GPU
+# See https://github.com/NVIDIA/spark-rapids/issues/3715
+# and https://github.com/rapidsai/cudf/issues/11222
+@ignore_order(local=True)
+@allow_non_gpu("ProjectExec", "SortArray")
+def test_window_aggs_for_rows_collect_set_nested_array():
+    conf = copy_and_update(_no_nans_float_conf, {
+        "spark.rapids.sql.castFloatToString.enabled": "true",
+        "spark.rapids.sql.castDecimalToString.enabled": "true",
+        "spark.rapids.sql.expression.SortArray": "false"
+    })
+
+    def do_it(spark):
+        df = gen_df(spark, _gen_data_for_collect_set_nested, length=512)
+        df.createOrReplaceTempView("window_collect_table")
+        df = spark.sql(
+            """select a, b,
+              collect_set(c_struct_array_1) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_struct_array_1, 
+              collect_set(c_struct_array_2) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_struct_array_2,
+              collect_set(c_array_struct) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_struct,
+              collect_set(c_array_array_bool) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_bool,
+              collect_set(c_array_array_int) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_int,
+              collect_set(c_array_array_long) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_long,
+              collect_set(c_array_array_short) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_short,
+              collect_set(c_array_array_date) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_date,
+              collect_set(c_array_array_timestamp) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_ts,
+              collect_set(c_array_array_byte) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_byte,
+              collect_set(c_array_array_string) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_str,
+              collect_set(c_array_array_float) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_float,
+              collect_set(c_array_array_double) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_double,
+              collect_set(c_array_array_decimal_32) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_decimal_32,
+              collect_set(c_array_array_decimal_64) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_decimal_64,
+              collect_set(c_array_array_decimal_128) over
+                (partition by a order by b,c_int rows between CURRENT ROW and UNBOUNDED FOLLOWING) as cc_array_array_decimal_128
+        from window_collect_table
+        """)
+        df = spark.createDataFrame(df.rdd, schema=df.schema)
+        # pull out the rdd and schema and create a new dataframe to run SortArray
+        # to handle Databricks 10.4+ optimization that moves SortArray from ProjectExec
+        # to ObjectHashAggregateExec
+        df.createOrReplaceTempView("window_collect_table_2")
+        return spark.sql("""select a, b,
+              sort_array(cc_struct_array_1),
+              sort_array(cc_struct_array_2),
+              sort_array(cc_array_struct),
+              sort_array(cc_array_array_bool),
+              sort_array(cc_array_array_int),
+              sort_array(cc_array_array_long),
+              sort_array(cc_array_array_short),
+              sort_array(cc_array_array_date),
+              sort_array(cc_array_array_ts),
+              sort_array(cc_array_array_byte),
+              sort_array(cc_array_array_str),
+              sort_array(cc_array_array_float),
+              sort_array(cc_array_array_double),
+              sort_array(cc_array_array_decimal_32),
+              sort_array(cc_array_array_decimal_64),
+              sort_array(cc_array_array_decimal_128)
+        from window_collect_table_2
+        """)
+    assert_gpu_and_cpu_are_equal_collect(do_it, conf=conf)
+
+
 # In a distributed setup the order of the partitions returned might be different, so we must ignore the order
 # but small batch sizes can make sort very slow, so do the final order by locally
 @ignore_order(local=True)
diff --git a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
index a13e3f69cd2..9299d3de487 100644
--- a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
+++ b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
@@ -3332,13 +3332,38 @@ object GpuOverrides extends Logging {
       "Collect a set of unique elements, not supported in reduction",
       ExprChecks.fullAgg(
         TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL_128 +
-            TypeSig.NULL + TypeSig.STRUCT),
+            TypeSig.NULL + TypeSig.STRUCT + TypeSig.ARRAY),
         TypeSig.ARRAY.nested(TypeSig.all),
         Seq(ParamCheck("input",
           (TypeSig.commonCudfTypes + TypeSig.DECIMAL_128 +
-              TypeSig.NULL + TypeSig.STRUCT).nested(),
+              TypeSig.NULL + 
+              TypeSig.STRUCT.withPsNote(TypeEnum.STRUCT, "Support for structs containing " +
+              s"float/double array columns requires ${RapidsConf.HAS_NANS} to be set to false") +
+              TypeSig.ARRAY.withPsNote(TypeEnum.ARRAY, "Support for arrays of arrays of " +
+              s"floats/doubles requires ${RapidsConf.HAS_NANS} to be set to false")).nested(),
           TypeSig.all))),
       (c, conf, p, r) => new TypedImperativeAggExprMeta[CollectSet](c, conf, p, r) {
+
+        private def isNestedArrayType(dt: DataType): Boolean = {
+          dt match {
+            case StructType(fields) => 
+              fields.exists { field =>
+                field.dataType match {
+                  case sdt: StructType => isNestedArrayType(sdt)
+                  case _: ArrayType => true
+                  case _ => false
+                }
+              }
+            case ArrayType(et, _) => et.isInstanceOf[ArrayType] || et.isInstanceOf[StructType]
+            case _ => false
+          }
+        }
+
+        override def tagAggForGpu(): Unit = {
+          if (isNestedArrayType(c.child.dataType)) {
+            checkAndTagFloatNanAgg("CollectSet", c.child.dataType, conf, this)
+          }
+        }
         override def convertToGpu(childExprs: Seq[Expression]): GpuExpression =
           GpuCollectSet(childExprs.head, c.mutableAggBufferOffset, c.inputAggBufferOffset)
 
diff --git a/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala
index 5c7701f4833..94e76a3ffb6 100644
--- a/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala
+++ b/sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala
@@ -1691,7 +1691,8 @@ case class GpuCollectSet(
 
   override def windowAggregation(
       inputs: Seq[(ColumnVector, Int)]): RollingAggregationOnColumn =
-    RollingAggregation.collectSet().onColumn(inputs.head._2)
+    RollingAggregation.collectSet(NullPolicy.EXCLUDE, NullEquality.EQUAL,
+        NaNEquality.UNEQUAL).onColumn(inputs.head._2)
 }
 
 trait CpuToGpuAggregateBufferConverter {
diff --git a/tools/src/main/resources/supportedExprs.csv b/tools/src/main/resources/supportedExprs.csv
index 1ae14023b5d..caabbaa7883 100644
--- a/tools/src/main/resources/supportedExprs.csv
+++ b/tools/src/main/resources/supportedExprs.csv
@@ -581,11 +581,11 @@ CollectList,S,`collect_list`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS
 CollectList,S,`collect_list`,None,reduction,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA
 CollectList,S,`collect_list`,None,window,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,PS,PS,PS,NS
 CollectList,S,`collect_list`,None,window,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA
-CollectSet,S,`collect_set`,None,aggregation,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,NS,NS,PS,NS
+CollectSet,S,`collect_set`,None,aggregation,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,PS,NS,PS,NS
 CollectSet,S,`collect_set`,None,aggregation,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA
-CollectSet,S,`collect_set`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,NS,NS,PS,NS
+CollectSet,S,`collect_set`,None,reduction,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,PS,NS,PS,NS
 CollectSet,S,`collect_set`,None,reduction,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA
-CollectSet,S,`collect_set`,None,window,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,NS,NS,PS,NS
+CollectSet,S,`collect_set`,None,window,input,S,S,S,S,S,S,S,S,PS,S,S,S,NS,NS,PS,NS,PS,NS
 CollectSet,S,`collect_set`,None,window,result,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,PS,NA,NA,NA
 Count,S,`count`,None,aggregation,input,S,S,S,S,S,S,S,S,PS,S,S,S,S,S,PS,PS,PS,S
 Count,S,`count`,None,aggregation,result,NA,NA,NA,NA,S,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA