diff --git a/python/cuml/cuml/neighbors/kernel_density.py b/python/cuml/cuml/neighbors/kernel_density.py
index 5cb03c664d..59fc4e0509 100644
--- a/python/cuml/cuml/neighbors/kernel_density.py
+++ b/python/cuml/cuml/neighbors/kernel_density.py
@@ -255,24 +255,26 @@ def fit(self, X, y=None, sample_weight=None, *, convert_dtype=True):
         self : object
             Returns the instance itself.
         """
+        self.X_ = input_to_cupy_array(
+            X,
+            order="C",
+            convert_to_dtype=(np.float32 if convert_dtype else None),
+            check_dtype=[cp.float32, cp.float64],
+        ).array
+
         if sample_weight is not None:
             self.sample_weight_ = input_to_cupy_array(
                 sample_weight,
                 convert_to_dtype=(np.float32 if convert_dtype else None),
                 check_dtype=[cp.float32, cp.float64],
+                check_cols=1,
+                check_rows=self.X_.shape[0],
             ).array
             if self.sample_weight_.min() <= 0:
                 raise ValueError("sample_weight must have positive values")
         else:
             self.sample_weight_ = None
 
-        self.X_ = input_to_cupy_array(
-            X,
-            order="C",
-            convert_to_dtype=(np.float32 if convert_dtype else None),
-            check_dtype=[cp.float32, cp.float64],
-        ).array
-
         return self
 
     def score_samples(self, X, *, convert_dtype=True):
diff --git a/python/cuml/tests/test_kernel_density.py b/python/cuml/tests/test_kernel_density.py
index adadf3e5bc..29a18af3c4 100644
--- a/python/cuml/tests/test_kernel_density.py
+++ b/python/cuml/tests/test_kernel_density.py
@@ -195,3 +195,16 @@ def test_not_fitted():
         kde.sample(X)
     with pytest.raises(NotFittedError):
         kde.score_samples(X)
+
+
+def test_bad_sample_weight_errors():
+    kde = KernelDensity()
+    X = np.array([[0.0, 1.0], [2.0, 0.5]])
+
+    with pytest.raises(ValueError, match="Expected 2 rows but got 3 rows."):
+        kde.fit(X, sample_weight=np.array([1, 2, 3]))
+
+    with pytest.raises(
+        ValueError, match="Expected 1 columns but got 2 columns."
+    ):
+        kde.fit(X, sample_weight=np.array([[1, 2], [3, 4]]))