diff --git a/sdk/python/feast/templates/gcp/driver_repo.py b/sdk/python/feast/templates/gcp/driver_repo.py
index 03ee6e1272..b36758046d 100644
--- a/sdk/python/feast/templates/gcp/driver_repo.py
+++ b/sdk/python/feast/templates/gcp/driver_repo.py
@@ -7,7 +7,7 @@
 # construction of feature vectors
 driver = Entity(
     # Name of the entity. Must be unique within a project
-    name="driver",
+    name="driver_id",
     # The join key of an entity describes the storage level field/column on which
     # features can be looked up. The join key is also used to join feature
     # tables/views when building feature vectors
@@ -20,7 +20,7 @@
 # datasets or materializing features into an online store.
 driver_stats_source = BigQuerySource(
     # The BigQuery table where features can be found
-    table_ref="feast-oss.demo_data.driver_stats",
+    table_ref="feast-oss.demo_data.driver_hourly_stats",
     # The event timestamp is used for point-in-time joins and for ensuring only
     # features within the TTL are returned
     event_timestamp_column="datetime",
@@ -34,11 +34,11 @@
 driver_stats_fv = FeatureView(
     # The unique name of this feature view. Two feature views in a single
     # project cannot have the same name
-    name="driver_stats",
+    name="driver_hourly_stats",
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver"],
+    entities=["driver_id"],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
diff --git a/sdk/python/feast/templates/gcp/test.py b/sdk/python/feast/templates/gcp/test.py
index 00bb4ae63c..aee8950077 100644
--- a/sdk/python/feast/templates/gcp/test.py
+++ b/sdk/python/feast/templates/gcp/test.py
@@ -18,7 +18,7 @@ def main():
     fs.apply([driver, driver_stats_fv])
 
     # Select features
-    feature_refs = ["driver_stats:conv_rate", "driver_stats:acc_rate"]
+    feature_refs = ["driver_hourly_stats:conv_rate", "driver_hourly_stats:acc_rate"]
 
     # Create an entity dataframe. This is the dataframe that will be enriched with historical features
     entity_df = pd.DataFrame(
@@ -31,7 +31,7 @@ def main():
                     periods=3,
                 )
             ],
-            "driver_id": [5001, 5002, 5003],
+            "driver_id": [1001, 1002, 1003],
         }
     )
 
@@ -54,7 +54,8 @@ def main():
 
     # Retrieve features from the online store (Firestore)
     online_features = fs.get_online_features(
-        feature_refs=feature_refs, entity_rows=[{"driver": 5001}, {"driver": 5002}]
+        feature_refs=feature_refs,
+        entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}],
     ).to_dict()
 
     print()