diff --git a/python/cuml/test/dask/test_naive_bayes.py b/python/cuml/test/dask/test_naive_bayes.py
index d00b9b9d7e..62cf819ce8 100644
--- a/python/cuml/test/dask/test_naive_bayes.py
+++ b/python/cuml/test/dask/test_naive_bayes.py
@@ -14,13 +14,15 @@
 # limitations under the License.
 #
 
-
-from cuml.test.dask.utils import load_text_corpus
-
-from sklearn.metrics import accuracy_score
+import cupy as cp
+import dask.array
+import numpy as np
 
 from cuml.dask.naive_bayes import MultinomialNB
 from cuml.naive_bayes.naive_bayes import MultinomialNB as SGNB
+from cuml.test.dask.utils import load_text_corpus
+from cupy.sparse import csr_matrix as cp_csr_matrix
+from sklearn.metrics import accuracy_score
 
 
 def test_basic_fit_predict(client):
@@ -74,3 +76,20 @@ def test_score(client):
     y_local = y.compute()
 
     assert(accuracy_score(y_hat_local.get(), y_local) == score)
+
+
+def test_model_multiple_chunks(client):
+    # tests naive_bayes with n_chunks being greater than one, related to issue
+    # https://github.com/rapidsai/cuml/issues/3150
+    X = cp.array([[0, 0, 0, 1], [1, 0, 0, 1], [1, 0, 0, 0]])
+
+    X = dask.array.from_array(X, chunks=((1, 1, 1), -1)).astype(cp.int32)
+    y = dask.array.from_array([1, 0, 0], asarray=False,
+                              fancy=False, chunks=(1)).astype(cp.int32)
+
+    model = MultinomialNB()
+    model.fit(X, y)
+
+    # this test is a code coverage test, it is too small to be a numeric test,
+    # but we call score here to exercise the whole model.
+    assert(0 <= model.score(X, y) <= 1)