diff --git a/python-package/xgboost/dask.py b/python-package/xgboost/dask.py
index 88bd1c819..35c5c009f 100644
--- a/python-package/xgboost/dask.py
+++ b/python-package/xgboost/dask.py
@@ -73,6 +73,7 @@ from .core import (
     _deprecate_positional_args,
     _expect,
 )
+from .data import _is_cudf_ser, _is_cupy_array
 from .sklearn import (
     XGBClassifier,
     XGBClassifierBase,
@@ -1894,10 +1895,15 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierMixIn, XGBClassifierBa
         )
 
         # pylint: disable=attribute-defined-outside-init
-        if isinstance(y, (da.Array)):
+        if isinstance(y, da.Array):
             self.classes_ = await self.client.compute(da.unique(y))
         else:
             self.classes_ = await self.client.compute(y.drop_duplicates())
+        if _is_cudf_ser(self.classes_):
+            self.classes_ = self.classes_.to_cupy()
+        if _is_cupy_array(self.classes_):
+            self.classes_ = self.classes_.get()
+        self.classes_ = numpy.array(self.classes_)
         self.n_classes_ = len(self.classes_)
 
         if self.n_classes_ > 2:
diff --git a/tests/test_distributed/test_with_dask/test_with_dask.py b/tests/test_distributed/test_with_dask/test_with_dask.py
index 0bf952025..5e9303a46 100644
--- a/tests/test_distributed/test_with_dask/test_with_dask.py
+++ b/tests/test_distributed/test_with_dask/test_with_dask.py
@@ -192,6 +192,25 @@ def deterministic_repartition(
     return X, y, m
 
 
+@pytest.mark.parametrize("to_frame", [True, False])
+def test_xgbclassifier_classes_type_and_value(to_frame: bool, client: "Client"):
+    X, y = make_classification(n_samples=1000, n_features=4, random_state=123)
+    if to_frame:
+        import pandas as pd
+        feats = [f"var_{i}" for i in range(4)]
+        df = pd.DataFrame(X, columns=feats)
+        df["target"] = y
+        df = dd.from_pandas(df, npartitions=1)
+        X, y = df[feats], df["target"]
+    else:
+        X = da.from_array(X)
+        y = da.from_array(y)
+
+    est = xgb.dask.DaskXGBClassifier(n_estimators=10).fit(X, y)
+    assert isinstance(est.classes_, np.ndarray)
+    np.testing.assert_array_equal(est.classes_, np.array([0, 1]))
+
+
 def test_from_dask_dataframe() -> None:
     with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
         with Client(cluster) as client: