diff --git a/tests/unit/loader/test_tf_dataloader.py b/tests/unit/loader/test_tf_dataloader.py
index e336ea650de..e1ba7bfe7c4 100644
--- a/tests/unit/loader/test_tf_dataloader.py
+++ b/tests/unit/loader/test_tf_dataloader.py
@@ -29,7 +29,6 @@
 from sklearn.metrics import roc_auc_score
 
 import nvtabular as nvt
-import nvtabular.tools.data_gen as datagen
 from nvtabular import ops
 
 tf = pytest.importorskip("tensorflow")
@@ -469,65 +468,6 @@ def test_multigpu_partitioning(datasets, engine, batch_size, global_rank):
     assert indices == [global_rank]
 
 
-@pytest.mark.parametrize("sparse_dense", [False, True])
-def test_sparse_tensors(tmpdir, sparse_dense):
-    # create small dataset, add values to sparse_list
-    json_sample = {
-        "conts": {},
-        "cats": {
-            "spar1": {
-                "dtype": None,
-                "cardinality": 50,
-                "min_entry_size": 1,
-                "max_entry_size": 5,
-                "multi_min": 2,
-                "multi_max": 4,
-                "multi_avg": 3,
-            },
-            "spar2": {
-                "dtype": None,
-                "cardinality": 50,
-                "min_entry_size": 1,
-                "max_entry_size": 5,
-                "multi_min": 3,
-                "multi_max": 5,
-                "multi_avg": 4,
-            },
-            # "": {"dtype": None, "cardinality": 500, "min_entry_size": 1, "max_entry_size": 5},
-        },
-        "labels": {"rating": {"dtype": None, "cardinality": 2}},
-    }
-    cols = datagen._get_cols_from_schema(json_sample)
-    df_gen = datagen.DatasetGen(datagen.UniformDistro(), gpu_frac=0.0001)
-    target_path = os.path.join(tmpdir, "input/")
-    os.mkdir(target_path)
-    df_files = df_gen.full_df_create(10000, cols, output=target_path)
-    spa_lst = ["spar1", "spar2"]
-    spa_mx = {"spar1": 5, "spar2": 6}
-    batch_size = 10
-    data_itr = tf_dataloader.KerasSequenceLoader(
-        df_files,
-        cat_names=spa_lst,
-        cont_names=[],
-        label_names=["rating"],
-        batch_size=batch_size,
-        buffer_size=0.1,
-        sparse_names=spa_lst,
-        sparse_max=spa_mx,
-        sparse_as_dense=sparse_dense,
-    )
-    for batch in data_itr:
-        feats, labs = batch
-        for col in spa_lst:
-            feature_tensor = feats[f"{col}"]
-            if not sparse_dense:
-                assert list(feature_tensor.shape) == [batch_size, spa_mx[col]]
-                assert isinstance(feature_tensor, tf.sparse.SparseTensor)
-            else:
-                assert feature_tensor.shape[1] == spa_mx[col]
-                assert not isinstance(feature_tensor, tf.sparse.SparseTensor)
-
-
 @pytest.mark.parametrize("batch_size", [1000])
 @pytest.mark.parametrize("engine", ["parquet"])
 @pytest.mark.parametrize("device", [None, 0])
diff --git a/tests/unit/loader/test_torch_dataloader.py b/tests/unit/loader/test_torch_dataloader.py
index 7754956bd3c..4d8c34dfa24 100644
--- a/tests/unit/loader/test_torch_dataloader.py
+++ b/tests/unit/loader/test_torch_dataloader.py
@@ -491,44 +491,6 @@ def test_mh_support(tmpdir):
     assert idx > 0
 
 
-@pytest.mark.parametrize("sparse_dense", [False, True])
-def test_sparse_tensors(sparse_dense):
-    # create small dataset, add values to sparse_list
-    df = make_df(
-        {
-            "spar1": [[1, 2, 3, 4], [4, 2, 4, 4], [1, 3, 4, 3], [1, 1, 3, 3]],
-            "spar2": [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14], [15, 16]],
-        }
-    )
-    spa_lst = ["spar1", "spar2"]
-    spa_mx = {"spar1": 5, "spar2": 6}
-    batch_size = 2
-    data_itr = torch_dataloader.TorchAsyncItr(
-        nvt.Dataset(df),
-        cats=spa_lst,
-        conts=[],
-        labels=[],
-        batch_size=batch_size,
-        sparse_names=spa_lst,
-        sparse_max=spa_mx,
-        sparse_as_dense=sparse_dense,
-    )
-    for batch in data_itr:
-        feats, labs = batch
-        for col in spa_lst:
-            feature_tensor = feats[col]
-            if not sparse_dense:
-                assert list(feature_tensor.shape) == [batch_size, spa_mx[col]]
-                assert feature_tensor.is_sparse
-            else:
-                assert feature_tensor.shape[1] == spa_mx[col]
-                assert not feature_tensor.is_sparse
-
-    # add dict sparse_max entry for each target
-    # iterate dataloader grab sparse columns
-    # ensure they are correct structurally
-
-
 @pytest.mark.parametrize("batch_size", [1000])
 @pytest.mark.parametrize("engine", ["parquet"])
 @pytest.mark.parametrize("device", [None, 0])