diff --git a/tests/python-gpu/test_gpu_data_iterator.py b/tests/python-gpu/test_gpu_data_iterator.py
index f4eaab15e..3a31c93b0 100644
--- a/tests/python-gpu/test_gpu_data_iterator.py
+++ b/tests/python-gpu/test_gpu_data_iterator.py
@@ -19,7 +19,7 @@ def test_gpu_single_batch() -> None:
 @given(
     strategies.integers(0, 1024),
     strategies.integers(1, 7),
-    strategies.integers(0, 13),
+    strategies.integers(0, 8),
     strategies.booleans(),
 )
 @settings(deadline=None, print_blob=True)
diff --git a/tests/python/test_data_iterator.py b/tests/python/test_data_iterator.py
index 5e0e46860..f4d424b83 100644
--- a/tests/python/test_data_iterator.py
+++ b/tests/python/test_data_iterator.py
@@ -92,6 +92,12 @@ def run_data_iterator(
         assert non_increasing(results_from_it["Train"]["rmse"])
 
     X, y, w = it.as_arrays()
+    if use_cupy:
+        _y = y.get()
+    else:
+        _y = y
+    np.testing.assert_allclose(Xy.get_label(), _y)
+
     Xy = xgb.DMatrix(X, y, weight=w)
     assert Xy.num_row() == n_samples_per_batch * n_batches
     assert Xy.num_col() == n_features
diff --git a/tests/python/testing.py b/tests/python/testing.py
index 28afb30b6..eb84605a2 100644
--- a/tests/python/testing.py
+++ b/tests/python/testing.py
@@ -12,6 +12,7 @@ from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
 import pytest
 import gc
 import xgboost as xgb
+from xgboost.core import ArrayLike
 import numpy as np
 from scipy import sparse
 import platform
@@ -212,13 +213,16 @@ class IteratorForTest(xgb.core.DataIter):
 
     def as_arrays(
         self,
-    ) -> Tuple[Union[np.ndarray, sparse.csr_matrix], np.ndarray, np.ndarray]:
+    ) -> Tuple[Union[np.ndarray, sparse.csr_matrix], ArrayLike, ArrayLike]:
         if isinstance(self.X[0], sparse.csr_matrix):
             X = sparse.vstack(self.X, format="csr")
         else:
             X = np.concatenate(self.X, axis=0)
         y = np.concatenate(self.y, axis=0)
-        w = np.concatenate(self.w, axis=0)
+        if self.w:
+            w = np.concatenate(self.w, axis=0)
+        else:
+            w = None
         return X, y, w