diff --git a/python/cuml/test/dask/test_nearest_neighbors.py b/python/cuml/test/dask/test_nearest_neighbors.py
index cde8a5032f..7047b61aa2 100644
--- a/python/cuml/test/dask/test_nearest_neighbors.py
+++ b/python/cuml/test/dask/test_nearest_neighbors.py
@@ -70,8 +70,7 @@ def _scale_rows(client, nrows):
     return n_workers * nrows
 
 
-@pytest.mark.parametrize("nrows", [unit_param(100),
-                                   unit_param(1e3),
+@pytest.mark.parametrize("nrows", [unit_param(300),
                                    quality_param(1e6),
                                    stress_param(5e8)])
 @pytest.mark.parametrize("ncols", [10, 30])
@@ -81,8 +80,8 @@ def _scale_rows(client, nrows):
                                          stress_param(100)])
 @pytest.mark.parametrize("n_parts", [unit_param(1), unit_param(5),
                                      quality_param(7), stress_param(50)])
-@pytest.mark.parametrize("streams_per_handle", [5, 10])
-@pytest.mark.parametrize("reverse_worker_order", [True, False])
+@pytest.mark.parametrize("streams_per_handle,reverse_worker_order",
+                         [(5, True), (10, False)])
 def test_compare_skl(nrows, ncols, nclusters, n_parts, n_neighbors,
                      streams_per_handle, reverse_worker_order, client):
 
diff --git a/python/cuml/test/test_dbscan.py b/python/cuml/test/test_dbscan.py
index b5c65c5ca8..0c9d5c8e88 100644
--- a/python/cuml/test/test_dbscan.py
+++ b/python/cuml/test/test_dbscan.py
@@ -311,8 +311,9 @@ def test_core_point_prop3():
 @pytest.mark.parametrize('datatype', [np.float32, np.float64])
 @pytest.mark.parametrize('use_handle', [True, False])
 @pytest.mark.parametrize('out_dtype', ["int32", np.int32, "int64", np.int64])
-def test_dbscan_propagation(datatype, use_handle, out_dtype):
-    X, y = make_blobs(5000, centers=1, cluster_std=8.0,
+@pytest.mark.parametrize('n_samples', [unit_param(500), stress_param(5000)])
+def test_dbscan_propagation(datatype, use_handle, out_dtype, n_samples):
+    X, y = make_blobs(n_samples, centers=1, cluster_std=8.0,
                       center_box=(-100.0, 100.0), random_state=8)
     X = X.astype(datatype)
 
diff --git a/python/cuml/test/test_umap.py b/python/cuml/test/test_umap.py
index c04d39845e..e03c00f361 100644
--- a/python/cuml/test/test_umap.py
+++ b/python/cuml/test/test_umap.py
@@ -231,7 +231,7 @@ def test_umap_fit_transform_trust(name, target_metric):
         data = wine.data
         labels = wine.target
     else:
-        data, labels = make_blobs(n_samples=5000, n_features=10,
+        data, labels = make_blobs(n_samples=500, n_features=10,
                                   centers=10, random_state=42)
 
     model = umap.UMAP(n_neighbors=10, min_dist=0.01,
@@ -336,8 +336,15 @@ def test_umap_fit_transform_against_fit_and_transform():
     assert joblib.hash(ft_embedding) != joblib.hash(fit_embedding_diff_input)
 
 
-@pytest.mark.parametrize('n_components', [2, 21, 25, 50])
-@pytest.mark.parametrize('random_state', [None, 8, np.random.RandomState(42)])
+@pytest.mark.parametrize('n_components,random_state',
+                         [unit_param(2, None),
+                          unit_param(2, 8),
+                          unit_param(2, np.random.RandomState(42)),
+                          unit_param(21, None),
+                          unit_param(21, np.random.RandomState(42)),
+                          unit_param(25, 8),
+                          unit_param(50, None),
+                          stress_param(50, 8)])
 def test_umap_fit_transform_reproducibility(n_components, random_state):
 
     n_samples = 8000
@@ -372,8 +379,15 @@ def get_embedding(n_components, random_state):
         assert mean_diff > 0.5
 
 
-@pytest.mark.parametrize('n_components', [2, 21, 25, 50])
-@pytest.mark.parametrize('random_state', [None, 8, np.random.RandomState(42)])
+@pytest.mark.parametrize('n_components,random_state',
+                         [unit_param(2, None),
+                          unit_param(2, 8),
+                          unit_param(2, np.random.RandomState(42)),
+                          unit_param(21, None),
+                          unit_param(25, 8),
+                          unit_param(25, np.random.RandomState(42)),
+                          unit_param(50, None),
+                          stress_param(50, 8)])
 def test_umap_transform_reproducibility(n_components, random_state):
 
     n_samples = 5000