diff --git a/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption.feature b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption.feature
index c8a9a4efc..de167bf6c 100644
--- a/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption.feature
+++ b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption.feature
@@ -1,23 +1,8 @@
-@pyspark_data_encryption @integration
+@pyspark_data_encryption
 Feature: Data encryption
     # Generated sample BDD specification/feature file - PLEASE ***DO*** MODIFY.
     # Originally generated from templates/behave.feature.vm.
 
-  Scenario: Get fields from native inbound collection with inbound record type (set([CustomData]))
-  Given a pipeline with native inbound collection and inbound record type
-  When field names are retrieved for the inbound record
-  Then a list of field names can be retrieved
-
-  Scenario: Get fields from native non-collection inbound without an inbound record type (DataFrame)
-  Given a pipeline with native non-collection inbound and no inbound record type
-  When field names are retrieved for the inbound record
-  Then a list of field names can be retrieved
-
-  Scenario: Get fields from native collection inbound without an inbound record type (set([DataFrame]))
-  Given a pipeline with native collection inbound and no inbound record type
-  When field names are retrieved for the set of inbound dataframes
-  Then each list of field names can be retrieved
-
   Scenario: Encrypt fields for native inbound with inbound record type (set([CustomData]))
   Given a pipeline with native inbound collection and inbound record type
   When encryption is called on the inbound record
@@ -37,22 +22,3 @@ Feature: Data encryption
   Given a pipeline with native inbound collection and inbound record type
   When AES encryption is requested
   Then the correct AES algorithm is applied to the data set
-
-  @integration
-  Scenario: Specifying the Vault encryption algorithm yields a custom field encrypted with the corresponding algorithm
-  Given a pipeline with native inbound collection and inbound record type
-  When Vault encryption is requested
-  Then the correct Vault algorithm is applied to the data set
-
-  @integration
-  Scenario: Specifying the Vault encryption algorithm yields a dataframe field encrypted with the corresponding algorithm
-  Given a pipeline with native non-collection inbound and no inbound record type
-  When Vault encryption is requested
-  Then the correct Vault algorithm is applied to the dataframe
-
-  @integration
-  Scenario: Encrypt fields for native collection inbound without an inbound record type (set([DataFrame]))
-  Given a pipeline with native collection inbound and no inbound record type
-  When Vault encryption is requested
-  Then the correct dataframe fields are vault encrypted for each dataframe
-
diff --git a/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption_vault.feature b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption_vault.feature
new file mode 100644
index 000000000..1fe5761c8
--- /dev/null
+++ b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/pyspark_data_encryption_vault.feature
@@ -0,0 +1,18 @@
+@pyspark_data_encryption @integration
+Feature: Data encryption
+
+  Scenario: Specifying the Vault encryption algorithm yields a custom field encrypted with the corresponding algorithm
+  Given a pipeline with native inbound collection and inbound record type
+  When Vault encryption is requested
+  Then the correct Vault algorithm is applied to the data set
+
+  Scenario: Specifying the Vault encryption algorithm yields a dataframe field encrypted with the corresponding algorithm
+  Given a pipeline with native non-collection inbound and no inbound record type
+  When Vault encryption is requested
+  Then the correct Vault algorithm is applied to the dataframe
+
+  Scenario: Encrypt fields for native collection inbound without an inbound record type (set([DataFrame]))
+  Given a pipeline with native collection inbound and no inbound record type
+  When Vault encryption is requested
+  Then the correct dataframe fields are vault encrypted for each dataframe
+
diff --git a/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/steps/pyspark_data_encryption_steps.py b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/steps/pyspark_data_encryption_steps.py
index 0c15e281b..19165de64 100644
--- a/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/steps/pyspark_data_encryption_steps.py
+++ b/test/test-mda-models/aissemble-test-data-delivery-pyspark-model/tests/features/steps/pyspark_data_encryption_steps.py
@@ -16,12 +16,7 @@
 """
 
 from behave import given, when, then  # pylint: disable=no-name-in-module
-from os import path, walk
 import nose.tools as nt
-import ast
-import sys
-import os
-from pathlib import Path
 from aissemble_test_data_delivery_pyspark_model.step.native_inbound_with_custom_types import (
     NativeInboundWithCustomTypes,
 )
@@ -31,12 +26,8 @@
 from aissemble_test_data_delivery_pyspark_model.step.native_inbound_with_custom_collection_type import (
     NativeInboundWithCustomCollectionType,
 )
-from aissemble_test_data_delivery_pyspark_model.step.native_inbound_with_custom_data_type_async import (
-    NativeInboundWithCustomDataTypeAsync,
-)
 from aissemble_test_data_delivery_pyspark_model.record.custom_data import CustomData
 from krausening.logging import LogManager
-from pyspark.sql.types import StructType, StructField, StringType
 
 logger = LogManager.get_instance().get_logger("DataEncryptionTest")
 
@@ -92,21 +83,6 @@ def step_impl(context):
     )
 
 
-@when("field names are retrieved for the inbound record")
-def step_impl(context):
-    context.input_fields = context.pipeline.get_fields_list(context.inbound)
-    logger.info(context.input_fields)
-    nt.eq_(len(context.input_fields), 1, "Wrong number of input fields")
-
-
-@when("field names are retrieved for the set of inbound dataframes")
-def step_impl(context):
-    for df in context.inbound:
-        context.input_fields = context.pipeline.get_fields_list(df)
-        logger.info(context.input_fields)
-        nt.eq_(len(context.input_fields), 1, "Wrong number of input fields")
-
-
 @when("encryption is called on the inbound record")
 def step_impl(context):
     context.encrypted_dataset = context.pipeline.apply_encryption_to_dataset(
@@ -125,16 +101,6 @@ def step_impl(context):
     logger.info(context.encrypted_dataset)
 
 
-@then("a list of field names can be retrieved")
-def step_impl(context):
-    nt.eq_(
-        context.input_fields[0],
-        "custom_field",
-        'Input field was not "custom_field".  Instead it was '
-        + context.input_fields[0],
-    )
-
-
 @then("the correct fields are encrypted")
 def step_impl(context):
     for record in context.encrypted_dataset: