diff --git a/monai/data/dataset.py b/monai/data/dataset.py
index bfb3d8b86d..b8ecd2b35a 100644
--- a/monai/data/dataset.py
+++ b/monai/data/dataset.py
@@ -614,8 +614,10 @@ class SmartCacheDataset(Randomizable, CacheDataset):
         4. Call `shutdown()` when training ends.
 
     Note:
-        This replacement will not work if setting the `multiprocessing_context` of DataLoader to `spawn`
-        or on windows(the default multiprocessing method is `spawn`) and setting `num_workers` greater than 0.
+        This replacement will not work for below cases:
+        1. Set the `multiprocessing_context` of DataLoader to `spawn`.
+        2. Run on windows(the default multiprocessing method is `spawn`) with `num_workers` greater than 0.
+        3. Set the `persistent_workers` of DataLoader to `True` with `num_workers` greater than 0.
 
         If using MONAI workflows, please add `SmartCacheHandler` to the handler list of trainer,
         otherwise, please make sure to call `start()`, `update_cache()`, `shutdown()` during training.
diff --git a/tests/test_handler_smartcache.py b/tests/test_handler_smartcache.py
index cfe68e98e2..b67f1226cd 100644
--- a/tests/test_handler_smartcache.py
+++ b/tests/test_handler_smartcache.py
@@ -9,6 +9,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 import unittest
 
 import torch
@@ -16,8 +17,10 @@
 
 from monai.data import SmartCacheDataset
 from monai.handlers import SmartCacheHandler
+from tests.utils import SkipIfBeforePyTorchVersion
 
 
+@SkipIfBeforePyTorchVersion((1, 7))
 class TestHandlerSmartCache(unittest.TestCase):
     def test_content(self):
         data = [0, 1, 2, 3, 4, 5, 6, 7, 8]
@@ -37,7 +40,8 @@ def _train_func(engine, batch):
 
         # set up testing handler
         dataset = SmartCacheDataset(data, transform=None, replace_rate=0.2, cache_num=5, shuffle=False)
-        data_loader = torch.utils.data.DataLoader(dataset, batch_size=5)
+        workers = 2 if sys.platform == "linux" else 0
+        data_loader = torch.utils.data.DataLoader(dataset, batch_size=5, num_workers=workers, persistent_workers=False)
         SmartCacheHandler(dataset).attach(engine)
 
         engine.run(data_loader, max_epochs=5)