diff --git a/python-package/xgboost/compat.py b/python-package/xgboost/compat.py
index 77480f79c8a0..954d04aea1ba 100644
--- a/python-package/xgboost/compat.py
+++ b/python-package/xgboost/compat.py
@@ -112,12 +112,10 @@ def from_json(self, doc):
 
 
 try:
-    import sparse
     import scipy.sparse as scipy_sparse
     from scipy.sparse import csr_matrix as scipy_csr
     SCIPY_INSTALLED = True
 except ImportError:
-    sparse = False
     scipy_sparse = False
     scipy_csr: Any = object
     SCIPY_INSTALLED = False
diff --git a/python-package/xgboost/dask.py b/python-package/xgboost/dask.py
index 2f945bff2ee0..6ae6d7bd959a 100644
--- a/python-package/xgboost/dask.py
+++ b/python-package/xgboost/dask.py
@@ -33,7 +33,7 @@
 from .callback import TrainingCallback
 
 from .compat import LazyLoader
-from .compat import sparse, scipy_sparse
+from .compat import scipy_sparse
 from .compat import PANDAS_INSTALLED, DataFrame, Series, pandas_concat
 from .compat import lazy_isinstance
 
@@ -186,10 +186,13 @@ def concat(value: Any) -> Any:  # pylint: disable=too-many-return-statements
     '''To be replaced with dask builtin.'''
     if isinstance(value[0], numpy.ndarray):
         return numpy.concatenate(value, axis=0)
+    if scipy_sparse and isinstance(value[0], scipy_sparse.csr_matrix):
+        return scipy_sparse.vstack(value, format="csr")
+    if scipy_sparse and isinstance(value[0], scipy_sparse.csc_matrix):
+        return scipy_sparse.vstack(value, format="csc")
     if scipy_sparse and isinstance(value[0], scipy_sparse.spmatrix):
+        # other sparse format will be converted to CSR.
         return scipy_sparse.vstack(value, format='csr')
-    if sparse and isinstance(value[0], sparse.SparseArray):
-        return sparse.concatenate(value, axis=0)
     if PANDAS_INSTALLED and isinstance(value[0], (DataFrame, Series)):
         return pandas_concat(value, axis=0)
     if lazy_isinstance(value[0], 'cudf.core.dataframe', 'DataFrame') or \
diff --git a/tests/python/test_with_dask.py b/tests/python/test_with_dask.py
index da9441aeed3e..dfca5b206511 100644
--- a/tests/python/test_with_dask.py
+++ b/tests/python/test_with_dask.py
@@ -7,7 +7,7 @@
 import numpy as np
 import scipy
 import json
-from typing import List, Tuple, Dict, Optional, Type, Any, Callable
+from typing import List, Tuple, Dict, Optional, Type, Any
 import asyncio
 from functools import partial
 from concurrent.futures import ThreadPoolExecutor
@@ -149,6 +149,30 @@ def test_from_dask_array() -> None:
             assert np.all(single_node_predt == from_arr.compute())
 
 
+def test_dask_sparse(client: "Client") -> None:
+    X_, y_ = make_classification(n_samples=1000, n_informative=5, n_classes=3)
+    rng = np.random.default_rng(seed=0)
+    idx = rng.integers(low=0, high=X_.shape[0], size=X_.shape[0] // 4)
+    X_[idx, :] = np.nan
+
+    # numpy
+    X, y = da.from_array(X_), da.from_array(y_)
+    clf = xgb.dask.DaskXGBClassifier(tree_method="hist", n_estimators=10)
+    clf.client = client
+    clf.fit(X, y, eval_set=[(X, y)])
+    dense_results = clf.evals_result()
+
+    # scipy sparse
+    X, y = da.from_array(X_).map_blocks(scipy.sparse.csr_matrix), da.from_array(y_)
+    clf = xgb.dask.DaskXGBClassifier(tree_method="hist", n_estimators=10)
+    clf.client = client
+    clf.fit(X, y, eval_set=[(X, y)])
+    sparse_results = clf.evals_result()
+    np.testing.assert_allclose(
+        dense_results["validation_0"]["mlogloss"], sparse_results["validation_0"]["mlogloss"]
+    )
+
+
 def test_dask_predict_shape_infer(client: "Client") -> None:
     X, y = make_classification(n_samples=1000, n_informative=5, n_classes=3)
     X_ = dd.from_array(X, chunksize=100)
@@ -270,7 +294,8 @@ def run_boost_from_prediction(
 def test_boost_from_prediction(tree_method: str, client: "Client") -> None:
     from sklearn.datasets import load_breast_cancer, load_digits
     X_, y_ = load_breast_cancer(return_X_y=True)
-    X, y = dd.from_array(X_, chunksize=100), dd.from_array(y_, chunksize=100)
+    X, y = dd.from_array(X_, chunksize=200), dd.from_array(y_, chunksize=200)
+
     run_boost_from_prediction(X, y, tree_method, client)
 
     X_, y_ = load_digits(return_X_y=True)