diff --git a/sdks/python/apache_beam/ml/transforms/handlers.py b/sdks/python/apache_beam/ml/transforms/handlers.py
index 864e1017255a..e92516531dd9 100644
--- a/sdks/python/apache_beam/ml/transforms/handlers.py
+++ b/sdks/python/apache_beam/ml/transforms/handlers.py
@@ -86,32 +86,30 @@
 
 
 class DataCoder:
-  def __init__(self, exclude_columns=None):
+  def __init__(
+      self,
+      exclude_columns,
+      coder=coders.registry.get_coder(Any),
+  ):
     """
     Uses PickleCoder to encode/decode the dictonaries.
     Args:
       exclude_columns: list of columns to exclude from the encoding.
     """
-    self.coder = coders.registry.get_coder(Any)
-    self.exclude_columns = exclude_columns
-
-  def set_unused_columns(self, exclude_columns):
+    self.coder = coder
     self.exclude_columns = exclude_columns
 
   def encode(self, element):
-    if not self.exclude_columns:
-      return element
+    # if not set(element.keys()) - set(self.exclude_columns):
+    #   return element
     data_to_encode = element.copy()
     for key in self.exclude_columns:
       if key in data_to_encode:
         del data_to_encode[key]
-
     element[_TEMP_KEY] = self.coder.encode(data_to_encode)
     return element
 
   def decode(self, element):
-    if not self.exclude_columns:
-      return element
     clone = copy.copy(element)
     clone.update(self.coder.decode(clone[_TEMP_KEY].item()))
     del clone[_TEMP_KEY]
@@ -176,7 +174,6 @@ def __init__(
     self.artifact_mode = artifact_mode
     if artifact_mode not in ['produce', 'consume']:
       raise ValueError('artifact_mode must be either `produce` or `consume`.')
-    self.data_coder = DataCoder()
 
   def append_transform(self, transform):
     self.transforms.append(transform)
@@ -365,7 +362,6 @@ def expand(
     artifact_location, which was previously used to store the produced
     artifacts.
     """
-
     if self.artifact_mode == ArtifactMode.PRODUCE:
       # If we are computing artifacts, we should fail for windows other than
       # default windowing since for example, for a fixed window, each window can
@@ -409,24 +405,29 @@ def expand(
       raw_data_metadata = metadata_io.read_metadata(
           os.path.join(self.artifact_location, RAW_DATA_METADATA_DIR))
 
-    keyed_raw_data = (raw_data)  #  | beam.ParDo(_ComputeAndAttachUniqueID()))
-
     feature_set = [feature.name for feature in raw_data_metadata.schema.feature]
-    self.data_coder.set_unused_columns(exclude_columns=feature_set)
+
+    # TFT ignores columns in the input data that aren't explicitly defined
+    # in the schema. This is because TFT operations
+    # are designed to work with a predetermined schema.
+    # To preserve these extra columns without disrupting TFT processing,
+    # they are temporarily encoded as bytes and added to the PCollection with
+    # a unique identifier
+    data_coder = DataCoder(exclude_columns=feature_set)
+    data_with_encoded_columns = (
+        raw_data
+        | "EncodeUnmodifiedColumns" >>
+        beam.Map(lambda elem: data_coder.encode(elem)))
 
     # To maintain consistency by outputting numpy array all the time,
     # whether a scalar value or list or np array is passed as input,
-    #  we will convert scalar values to list values and TFT will ouput
+    # we will convert scalar values to list values and TFT will ouput
     # numpy array all the time.
-    raw_data_list = (
-        keyed_raw_data
-        | beam.Map(lambda elem: self.data_coder.encode(elem)))
-
-    keyed_raw_data = keyed_raw_data | beam.ParDo(
+    data_list = data_with_encoded_columns | beam.ParDo(
         _ConvertScalarValuesToListValues())
 
     with tft_beam.Context(temp_dir=self.artifact_location):
-      data = (raw_data_list, raw_data_metadata)
+      data = (data_list, raw_data_metadata)
       if self.artifact_mode == ArtifactMode.PRODUCE:
         transform_fn = (
             data
@@ -436,7 +437,7 @@ def expand(
         self.write_transform_artifacts(transform_fn, self.artifact_location)
       else:
         transform_fn = (
-            raw_data_list.pipeline
+            data_list.pipeline
             | "ReadTransformFn" >> tft_beam.ReadTransformFn(
                 self.artifact_location))
       (transformed_dataset, transformed_metadata) = (
@@ -458,9 +459,11 @@ def expand(
       row_type = RowTypeConstraint.from_fields(
           list(self.transformed_schema.items()))
 
+      # Decode the extra columns that were encoded as bytes.
       transformed_dataset = (
           transformed_dataset
-          | "DecodeDict" >> beam.Map(lambda x: self.data_coder.decode(x)))
+          |
+          "DecodeUnmodifiedColumns" >> beam.Map(lambda x: data_coder.decode(x)))
       # The schema only contains the columns that are transformed.
       transformed_dataset = (
           transformed_dataset