diff --git a/tests/activities/test_activities.py b/tests/activities/test_activities.py
index 43fb648..b0d24b0 100644
--- a/tests/activities/test_activities.py
+++ b/tests/activities/test_activities.py
@@ -298,5 +298,10 @@ def test_activities_del_with_engine_exception_caught(
         def __del__(self):
             raise RuntimeError('Test error')
 
+    # Suppress the PytestUnraisableExceptionWarning for this specific test
+    import warnings
+
+    warnings.filterwarnings('ignore', category=pytest.PytestUnraisableExceptionWarning)
+
     with patch('builtins.super', return_value=MockSuperWithError()):
         activities.__del__()
diff --git a/tests/activities/test_experiment_tracking.py b/tests/activities/test_experiment_tracking.py
index 817369c..2d86f88 100644
--- a/tests/activities/test_experiment_tracking.py
+++ b/tests/activities/test_experiment_tracking.py
@@ -143,6 +143,11 @@ def test_experiment_tracking_del_with_engine_exception(
         def __del__(self):
             raise RuntimeError('Test error')
 
+    # Suppress the PytestUnraisableExceptionWarning for this specific test
+    import warnings
+
+    warnings.filterwarnings('ignore', category=pytest.PytestUnraisableExceptionWarning)
+
     with patch('builtins.super', return_value=MockSuperWithError()):
         et.__del__()
 
diff --git a/tests/utils/repository/test_training_repository.py b/tests/utils/repository/test_training_repository.py
index b89bd54..6429748 100644
--- a/tests/utils/repository/test_training_repository.py
+++ b/tests/utils/repository/test_training_repository.py
@@ -304,12 +304,12 @@ class TestAfterTrainCalculation:
         x_train = pd.DataFrame(
             {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
         )
-        x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3])
+        x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
         y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
-        y_test = pd.Series([400], index=[3], name='target')
+        y_test = pd.Series([400, 500], index=[3, 4], name='target')
 
         # Mock predict to return a simple array
-        sample_linear_model.predict = MagicMock(return_value=np.array([450.0]))
+        sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
 
         return TrainModelResult(
             params=sample_params,
@@ -388,17 +388,17 @@ class TestAfterTrainCalculation:
         x_train = pd.DataFrame(
             {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
         )
-        x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3])
+        x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
         y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
-        y_test = pd.Series([400], index=[3], name='target')
+        y_test = pd.Series([400, 500], index=[3, 4], name='target')
 
-        # Mock predict
-        sample_linear_model.predict = MagicMock(return_value=np.array([450.0]))
+        # Mock predict to return a simple array
+        sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
 
         # Create mock scaler with denormalize methods
         mock_scaler = MagicMock()
         mock_scaler.denormalize_single_input = MagicMock(side_effect=lambda x, col: x * 2)
-        mock_scaler.denormalize_predictions = MagicMock(return_value=np.array([900.0]))
+        mock_scaler.denormalize_predictions = MagicMock(return_value=np.array([900.0, 1100.0]))
 
         # Create mock preprocessor
         mock_process_data = MagicMock()
@@ -432,12 +432,12 @@ class TestAfterTrainCalculation:
         x_train = pd.DataFrame(
             {'var1': [1, 2, 3], 'var2': [4, 5, 6], 'var3': [7, 8, 9]}, index=[0, 1, 2]
         )
-        x_test = pd.DataFrame({'var1': [10], 'var2': [11], 'var3': [12]}, index=[3])
+        x_test = pd.DataFrame({'var1': [10, 11], 'var2': [11, 12], 'var3': [12, 13]}, index=[3, 4])
         y_train = pd.Series([100, 200, 300], index=[0, 1, 2], name='target')
-        y_test = pd.Series([400], index=[3], name='target')
+        y_test = pd.Series([400, 500], index=[3, 4], name='target')
 
         # Mock predict
-        sample_linear_model.predict = MagicMock(return_value=np.array([450.0]))
+        sample_linear_model.predict = MagicMock(return_value=np.array([450.0, 550.0]))
 
         # Create mock sklearn scaler (without denormalize methods)
         mock_scaler = MagicMock()