@@ -115,14 +115,17 @@ def camel_to_snake(name):
 name = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
 return re.sub("([a-z0-9])([A-Z])", r"\1_\2", name).lower()
 
+output_names = []
 for transform_col in bq_model._properties["transformColumns"]:
+transform_col_dict = cast(dict, transform_col)
 # pass the columns that are not transformed
-if "transformSql" not in transform_col:
+if "transformSql" not in transform_col_dict:
 continue
-transform_sql: str = cast(dict, transform_col)["transformSql"]
+transform_sql: str = transform_col_dict["transformSql"]
 if not transform_sql.startswith("ML."):
 continue
 
+output_names.append(transform_col_dict["name"])
 found_transformer = False
 for prefix in _BQML_TRANSFROM_TYPE_MAPPING:
 if transform_sql.startswith(prefix):
@@ -141,7 +144,10 @@ def camel_to_snake(name):
 f"Unsupported transformer type. {constants.FEEDBACK_LINK}"
 )
 
-return cls(transformers=transformers)
+transformer = cls(transformers=transformers)
+transformer._output_names = output_names
+
+return transformer
 
 def _merge(
 self, bq_model: bigquery.Model
@@ -164,6 +170,7 @@ def _merge(
 for feature_column in bq_model.feature_columns
 ]
 ) == sorted(columns):
+transformer_0._output_names = self._output_names
 return transformer_0
 
 return self

@@ -142,3 +142,26 @@ def test_columntransformer_save_load(new_penguins_df, dataset_id):
 ]
 assert reloaded_transformer.transformers_ == expected
 assert reloaded_transformer._bqml_model is not None
+
+result = transformer.fit_transform(
+new_penguins_df[["species", "culmen_length_mm", "flipper_length_mm"]]
+).to_pandas()
+
+expected = pandas.DataFrame(
+{
+"onehotencoded_species": [
+[{"index": 1, "value": 1.0}],
+[{"index": 1, "value": 1.0}],
+[{"index": 2, "value": 1.0}],
+],
+"standard_scaled_culmen_length_mm": [
+1.313249,
+-0.20198,
+-1.111118,
+],
+"standard_scaled_flipper_length_mm": [1.251098, -1.196588, -0.054338],
+},
+index=pandas.Index([1633, 1672, 1690], dtype="Int64", name="tag_number"),
+)
+
+pandas.testing.assert_frame_equal(result, expected, rtol=0.1, check_dtype=False)

@@ -58,7 +58,7 @@ def test_standard_scaler_normalizes(penguins_df_default_index, new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_standard_scaler_normalizeds_fit_transform(new_penguins_df):
@@ -82,7 +82,7 @@ def test_standard_scaler_normalizeds_fit_transform(new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_standard_scaler_series_normalizes(penguins_df_default_index, new_penguins_df):
@@ -110,7 +110,7 @@ def test_standard_scaler_series_normalizes(penguins_df_default_index, new_pengui
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_standard_scaler_save_load(new_penguins_df, dataset_id):
@@ -125,6 +125,22 @@ def test_standard_scaler_save_load(new_penguins_df, dataset_id):
 assert isinstance(reloaded_transformer, preprocessing.StandardScaler)
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.transform(
+new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
+).to_pandas()
+
+expected = pd.DataFrame(
+{
+"standard_scaled_culmen_length_mm": [1.313249, -0.20198, -1.111118],
+"standard_scaled_culmen_depth_mm": [1.17072, -1.272416, 0.101848],
+"standard_scaled_flipper_length_mm": [1.251089, -1.196588, -0.054338],
+},
+dtype="Float64",
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
+
 
 def test_max_abs_scaler_normalizes(penguins_df_default_index, new_penguins_df):
 # TODO(http://b/292431644): add a second test that compares output to sklearn.preprocessing.MaxAbsScaler, when BQML's change is in prod.
@@ -157,7 +173,7 @@ def test_max_abs_scaler_normalizes(penguins_df_default_index, new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_max_abs_scaler_normalizeds_fit_transform(new_penguins_df):
@@ -176,7 +192,7 @@ def test_max_abs_scaler_normalizeds_fit_transform(new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_max_abs_scaler_series_normalizes(penguins_df_default_index, new_penguins_df):
@@ -199,7 +215,7 @@ def test_max_abs_scaler_series_normalizes(penguins_df_default_index, new_penguin
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_max_abs_scaler_save_load(new_penguins_df, dataset_id):
@@ -214,6 +230,22 @@ def test_max_abs_scaler_save_load(new_penguins_df, dataset_id):
 assert isinstance(reloaded_transformer, preprocessing.MaxAbsScaler)
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.transform(
+new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
+).to_pandas()
+
+expected = pd.DataFrame(
+{
+"max_abs_scaled_culmen_length_mm": [1.0, 0.974684, 0.959494],
+"max_abs_scaled_culmen_depth_mm": [1.0, 0.914894, 0.962766],
+"max_abs_scaled_flipper_length_mm": [1.0, 0.923469, 0.959184],
+},
+dtype="Float64",
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
+
 
 def test_min_max_scaler_normalized_fit_transform(new_penguins_df):
 scaler = preprocessing.MinMaxScaler()
@@ -231,7 +263,7 @@ def test_min_max_scaler_normalized_fit_transform(new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_min_max_scaler_series_normalizes(penguins_df_default_index, new_penguins_df):
@@ -255,7 +287,7 @@ def test_min_max_scaler_series_normalizes(penguins_df_default_index, new_penguin
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_min_max_scaler_normalizes(penguins_df_default_index, new_penguins_df):
@@ -290,7 +322,7 @@ def test_min_max_scaler_normalizes(penguins_df_default_index, new_penguins_df):
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_min_max_scaler_save_load(new_penguins_df, dataset_id):
@@ -305,6 +337,22 @@ def test_min_max_scaler_save_load(new_penguins_df, dataset_id):
 assert isinstance(reloaded_transformer, preprocessing.MinMaxScaler)
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.fit_transform(
+new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
+).to_pandas()
+
+expected = pd.DataFrame(
+{
+"min_max_scaled_culmen_length_mm": [1.0, 0.375, 0.0],
+"min_max_scaled_culmen_depth_mm": [1.0, 0.0, 0.5625],
+"min_max_scaled_flipper_length_mm": [1.0, 0.0, 0.466667],
+},
+dtype="Float64",
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
+
 
 def test_k_bins_discretizer_normalized_fit_transform_default_params(new_penguins_df):
 discretizer = preprocessing.KBinsDiscretizer(strategy="uniform")
@@ -322,7 +370,7 @@ def test_k_bins_discretizer_normalized_fit_transform_default_params(new_penguins
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_k_bins_discretizer_series_normalizes(
@@ -344,7 +392,7 @@ def test_k_bins_discretizer_series_normalizes(
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_k_bins_discretizer_normalizes(penguins_df_default_index, new_penguins_df):
@@ -374,7 +422,7 @@ def test_k_bins_discretizer_normalizes(penguins_df_default_index, new_penguins_d
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_k_bins_discretizer_normalizes_different_params(
@@ -406,7 +454,7 @@ def test_k_bins_discretizer_normalizes_different_params(
 index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
 )
 
-pd.testing.assert_frame_equal(result, expected, rtol=1e-3)
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
 
 
 def test_k_bins_discretizer_save_load(new_penguins_df, dataset_id):
@@ -423,6 +471,22 @@ def test_k_bins_discretizer_save_load(new_penguins_df, dataset_id):
 assert reloaded_transformer.strategy == transformer.strategy
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.fit_transform(
+new_penguins_df[["culmen_length_mm", "culmen_depth_mm", "flipper_length_mm"]]
+).to_pandas()
+
+expected = pd.DataFrame(
+{
+"kbinsdiscretizer_culmen_length_mm": ["bin_6", "bin_4", "bin_2"],
+"kbinsdiscretizer_culmen_depth_mm": ["bin_6", "bin_2", "bin_5"],
+"kbinsdiscretizer_flipper_length_mm": ["bin_6", "bin_2", "bin_4"],
+},
+dtype="string[pyarrow]",
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected, rtol=0.1)
+
 
 def test_one_hot_encoder_default_params(new_penguins_df):
 encoder = preprocessing.OneHotEncoder()
@@ -560,6 +624,29 @@ def test_one_hot_encoder_save_load(new_penguins_df, dataset_id):
 assert reloaded_transformer.max_categories == transformer.max_categories
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.fit_transform(
+new_penguins_df[["species", "sex"]]
+).to_pandas()
+
+expected = pd.DataFrame(
+{
+"onehotencoded_species": [
+[{"index": 1, "value": 1.0}],
+[{"index": 1, "value": 1.0}],
+[{"index": 2, "value": 1.0}],
+],
+"onehotencoded_sex": [
+[{"index": 2, "value": 1.0}],
+[{"index": 1, "value": 1.0}],
+[{"index": 1, "value": 1.0}],
+],
+},
+dtype=ONE_HOT_ENCODED_DTYPE,
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected)
+
 
 def test_label_encoder_default_params(new_penguins_df):
 encoder = preprocessing.LabelEncoder()
@@ -677,5 +764,21 @@ def test_label_encoder_save_load(new_penguins_df, dataset_id):
 assert reloaded_transformer.max_categories == transformer.max_categories
 assert reloaded_transformer._bqml_model is not None
 
+result = reloaded_transformer.transform(new_penguins_df).to_pandas()
+
+expected = pd.DataFrame(
+{
+"labelencoded_species": [
+1,
+1,
+2,
+],
+},
+dtype="Int64",
+index=pd.Index([1633, 1672, 1690], name="tag_number", dtype="Int64"),
+)
+
+pd.testing.assert_frame_equal(result, expected)
+
 
 # TODO(garrettwu): add OneHotEncoder tests to compare with sklearn.