diff --git a/tests/st/numpy_native/test_array_ops.py b/tests/st/numpy_native/test_array_ops.py
index 0bf6ec084b8..a8ed42c0b6b 100644
--- a/tests/st/numpy_native/test_array_ops.py
+++ b/tests/st/numpy_native/test_array_ops.py
@@ -534,12 +534,8 @@ def onp_vstack(input_array):
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_vstack():
-    onp_seq_lst0, mnp_seq_lst0 = prepare_array_sequences(
-        n_lst=[1, 5], ndim_lst=[2, 3, 4], axis=0)
-    onp_seq_lst1, mnp_seq_lst1 = prepare_array_sequences(
-        n_lst=[1, 5], ndim_lst=[1])
-    onp_seq_lst = onp_seq_lst0 + onp_seq_lst1
-    mnp_seq_lst = mnp_seq_lst0 + mnp_seq_lst1
+    onp_seq_lst, mnp_seq_lst = prepare_array_sequences(
+        n_lst=[1], ndim_lst=[2], axis=0)
     for i, onp_seq in enumerate(onp_seq_lst):
         mnp_seq = mnp_seq_lst[i]
         o_vstack = onp_vstack(onp_seq)
@@ -1596,8 +1592,8 @@ def test_piecewise():
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_unravel_index():
-    shapes = [(), 1, 3, (5, 1), (2, 6, 3)]
-    dims = [(5, 4, 7), (5*4, 7), 5*4*7]
+    shapes = [(2, 6, 3)]
+    dims = [(5, 4, 7), 5*4*7]
     for shape in shapes:
         x = onp.random.randint(0, 5*4*7, shape)
         for dim in dims:
diff --git a/tests/st/numpy_native/test_math_ops.py b/tests/st/numpy_native/test_math_ops.py
index 42aabd3aed3..6d76b4dd604 100644
--- a/tests/st/numpy_native/test_math_ops.py
+++ b/tests/st/numpy_native/test_math_ops.py
@@ -2206,25 +2206,13 @@ def test_histogramdd():
     y = [onp.random.randint(-10, 10, 5), onp.random.randint(-10, 10, 5), onp.random.randint(-10, 10, 5)]
     mnp_y = list(map(to_tensor, y))
     weights = onp.random.randn(5)
-    for bins in [(15, 4, 9), 10, [onp.arange(5).tolist(), onp.arange(3, 6).tolist(),
-                                  onp.arange(10, 20).tolist()]]:
+    for bins in [(15, 4, 9), 10]:
         # pylint: disable=redefined-builtin
         for range in [None, [[0, 5], [2, 7], [1, 3]]]:
             mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range)
             onp_res = onp.histogramdd(x, bins=bins, range=range)
             match_all_arrays(mnp_res[0], onp_res[0], error=1)
             match_all_arrays(mnp_res[1], onp_res[1], error=1)
-            mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, density=True)
-            onp_res = onp.histogramdd(x, bins=bins, range=range, density=True)
-            match_all_arrays(mnp_res[0], onp_res[0], error=1)
-            match_all_arrays(mnp_res[1], onp_res[1], error=1)
-            mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range, weights=to_tensor(weights))
-            onp_res = onp.histogramdd(x, bins=bins, range=range, weights=weights)
-            match_all_arrays(mnp_res[0], onp_res[0], error=1)
-            match_all_arrays(mnp_res[1], onp_res[1], error=1)
-            mnp_res = mnp.histogramdd(to_tensor(x), bins=bins, range=range,
-                                      weights=to_tensor(weights), density=True)
-
             mnp_res = mnp.histogramdd(mnp_y, bins=bins, range=range, weights=to_tensor(weights),
                                       density=True)
             onp_res = onp.histogramdd(y, bins, range=range, weights=weights, density=True)
@@ -2249,16 +2237,10 @@ def test_histogram2d():
     y = onp.random.randint(-10, 10, 10)
 
     weights = onp.random.randn(10)
-    for bins in [(5, 7), 4, [onp.arange(5).tolist(), onp.arange(2, 10).tolist()], [8, [1, 2, 3]]]:
+    for bins in [4, [8, [1, 2, 3]]]:
         # pylint: disable=redefined-builtin
         for range in [None, [(3, 3), (2, 20)]]:
             match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, error=1)
-            match_res(mnp.histogram2d, onp.histogram2d, x, y, bins=bins, range=range, density=True,
-                      error=1)
-            mnp_res = mnp.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range,
-                                      weights=to_tensor(weights))
-            onp_res = onp.histogram2d(x, y, bins=bins, range=range, weights=weights)
-            match_all_arrays(mnp_res, onp_res, error=1)
             mnp_res = mnp.histogram2d(to_tensor(x), to_tensor(y), bins=bins, range=range,
                                       weights=to_tensor(weights), density=True)
             onp_res = onp.histogram2d(x, y, bins=bins, range=range, weights=weights, density=True)
@@ -2626,19 +2608,11 @@ def test_ravel_multi_index():
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_norm():
-    arrs = [rand_int(1), rand_int(9), rand_int(6, 4), rand_int(5, 2, 3, 7)]
+    arrs = [rand_int(5, 2, 3, 7)]
     for x in arrs:
         for keepdims in [True, False]:
             match_res(mnp.norm, onp.linalg.norm, x, keepdims=keepdims, error=3)
 
-    axes = [None, -1, 1, 2]
-    order = [None, float('inf'), -float('inf'), 0, 1, -1, 2, -2, 3.7, -5, 3]
-    for x, axis in zip(arrs, axes):
-        # pylint: disable=redefined-builtin
-        for ord in order:
-            for keepdims in [True, False]:
-                match_res(mnp.norm, onp.linalg.norm, x, ord=ord, axis=axis, keepdims=keepdims, error=3)
-
     x = rand_int(3, 6, 4, 5)
     axes = [(0, 1), (0, 3), (1, 3), (2, 3)]
     order = [None, 'fro', float('inf'), -float('inf'), 1, -1]