diff --git a/breadbox/tests/api/test_types.py b/breadbox/tests/api/test_types.py
index b23c1e4be..040255c7b 100644
--- a/breadbox/tests/api/test_types.py
+++ b/breadbox/tests/api/test_types.py
@@ -419,7 +419,6 @@ def test_get_dimension_type_dimension_identifiers(
         {"id": "sample-1", "label": "Sample 1"},
     ]
 
-
     # test dim type doesn't exist
     nonexistent_dim_type_res = client.get(
         f"types/dimensions/nonexistentDimensionType/identifiers", headers=admin_headers,
@@ -459,7 +458,7 @@ def test_get_dimension_type_dimension_identifiers(
     assert len(only_dims_in_dataset) == 1
     assert only_dims_in_dataset[0]["id"] == "sample-1"
 
-    # verify all identifiers for a specific data type are returned. Additionally, only identifiers that are defined in metadata are returned
+    # verify all identifiers for a specific data type are returned. Additionally, only identifiers that are defined in metadata are returned since dimension type identifiers in its metadata would not share the same data type
     all_dims_for_data_type_res = client.get(
         f"types/dimensions/{dim_type_fields['name']}/identifiers?data_type=dataType1",
         headers=admin_headers,
@@ -471,11 +470,42 @@ def test_get_dimension_type_dimension_identifiers(
 
     # Test case if data type is metadata and show only dimensions in datasets is True. We expect that it returns an empty list since only the metadata dataset uses any identifiers
     res = client.get(
-        f"types/dimensions/{dim_type_fields['name']}/identifiers?data_type=metadata?show_only_dimensions_in_datasets=True",
+        f"types/dimensions/{dim_type_fields['name']}/identifiers?data_type=metadata&show_only_dimensions_in_datasets=True",
         headers=admin_headers,
     )
     assert res.json() == []
 
+    # Test case if data type is metadata and show only dimensions in datasets is True when there is a dataset of data type metadata with requested dimension type but is not used as a dimension types's metadata. The result should still filter out identifiers that are not found in the dimension type's metadata
+    matrix_values = factories.matrix_csv_data_file_with_values(
+        feature_ids=["feature-1", "feature-2", "feature-3"],
+        sample_ids=["sample-1", "sample-3"],
+        values=np.array([[1, 2, 3], [4, 5, 6]]),
+    )
+    matrix_dataset_metadata = factories.matrix_dataset(
+        db,
+        settings,
+        sample_type=dim_type_fields["name"],
+        data_file=matrix_values,
+        data_type="metadata",
+    )
+    res = client.get(
+        f"types/dimensions/{dim_type_fields['name']}/identifiers?data_type=metadata&show_only_dimensions_in_datasets=True",
+        headers=admin_headers,
+    )
+    assert res.json() == [
+        {"id": "sample-1", "label": "Sample 1"},
+    ]
+
+    # Test case if data type is metadata and show only dimensions in datasets is False when there is a dataset of data type metadata with requested dimension type but is not used as a dimension types's metadata. This should return dimension identifiers from both datasets but should still filter out identifiers that are not found in the dimension type's metadata
+    res = client.get(
+        f"types/dimensions/{dim_type_fields['name']}/identifiers?data_type=metadata",
+        headers=admin_headers,
+    )
+    assert sorted(res.json(), key=lambda item: item["id"]) == [
+        {"id": "sample-1", "label": "Sample 1"},
+        {"id": "sample-2", "label": "Sample 2"},
+    ]
+
 
 def test_invalid_dimension_type_metadata(client: TestClient, minimal_db, settings):
     db = minimal_db