diff --git a/tests/tensorflow2/utils.py b/tests/tensorflow2/utils.py
index 124da16b8..0cfec9217 100644
--- a/tests/tensorflow2/utils.py
+++ b/tests/tensorflow2/utils.py
@@ -15,3 +15,9 @@ def is_tf_2_2():
     if version.parse(tf.__version__) >= version.parse("2.2.0"):
         return True
     return False
+
+
+def is_tf_2_3():
+    if version.parse(tf.__version__) == version.parse("2.3.0"):
+        return True
+    return False
diff --git a/tests/zero_code_change/test_tensorflow2_integration.py b/tests/zero_code_change/test_tensorflow2_integration.py
index 026fef36b..cb3cc7ddb 100644
--- a/tests/zero_code_change/test_tensorflow2_integration.py
+++ b/tests/zero_code_change/test_tensorflow2_integration.py
@@ -20,6 +20,7 @@
 # Third Party
 import pytest
 import tensorflow.compat.v2 as tf
+from tests.tensorflow2.utils import is_tf_2_3
 from tests.utils import SagemakerSimulator
 
 # First Party
@@ -50,20 +51,29 @@ def helper_test_keras_v2(script_mode: bool = False, eager_mode: bool = True):
     """ Test the default ZCC behavior of saving losses and metrics in eager and non-eager modes."""
     smd.del_hook()
     tf.keras.backend.clear_session()
-    if not eager_mode:
+    if not eager_mode and is_tf_2_3() is False:
+        # v1 training APIs are currently not supported
+        # in ZCC mode with smdebug 0.9 and AWS TF 2.3.0
         tf.compat.v1.disable_eager_execution()
     enable_tb = False if tf.__version__ == "2.0.2" else True
     with SagemakerSimulator(enable_tb=enable_tb) as sim:
         model = get_keras_model_v2()
         (x_train, y_train), (x_test, y_test) = get_keras_data()
         x_train, x_test = x_train / 255, x_test / 255
+        run_eagerly = None
+        if is_tf_2_3():
+            # Test eager and non eager mode for v2
+            run_eagerly = eager_mode
 
         opt = tf.keras.optimizers.RMSprop()
         if script_mode:
             hook = smd.KerasHook(out_dir=sim.out_dir, export_tensorboard=True)
             opt = hook.wrap_optimizer(opt)
             model.compile(
-                loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"]
+                loss="sparse_categorical_crossentropy",
+                optimizer=opt,
+                metrics=["accuracy"],
+                run_eagerly=run_eagerly,
             )
             history = model.fit(
                 x_train, y_train, batch_size=64, epochs=2, validation_split=0.2, callbacks=[hook]
@@ -71,7 +81,10 @@ def helper_test_keras_v2(script_mode: bool = False, eager_mode: bool = True):
             test_scores = model.evaluate(x_test, y_test, verbose=2, callbacks=[hook])
         else:
             model.compile(
-                loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"]
+                loss="sparse_categorical_crossentropy",
+                optimizer=opt,
+                metrics=["accuracy"],
+                run_eagerly=run_eagerly,
             )
             history = model.fit(x_train, y_train, batch_size=64, epochs=2, validation_split=0.2)
             test_scores = model.evaluate(x_test, y_test, verbose=2)
@@ -101,7 +114,9 @@ def helper_test_keras_v2_json_config(
     """ Tests ZCC with custom hook configs """
     smd.del_hook()
     tf.keras.backend.clear_session()
-    if not eager_mode:
+    if not eager_mode and is_tf_2_3() is False:
+        # v1 training APIs are currently not supported
+        # in ZCC mode with smdebug 0.9 and AWS TF 2.3.0
         tf.compat.v1.disable_eager_execution()
     enable_tb = False if tf.__version__ == "2.0.2" else True
     with SagemakerSimulator(json_file_contents=json_file_contents, enable_tb=enable_tb) as sim:
@@ -110,11 +125,18 @@ def helper_test_keras_v2_json_config(
         x_train, x_test = x_train / 255, x_test / 255
 
         opt = tf.keras.optimizers.RMSprop()
+        run_eagerly = None
+        if is_tf_2_3():
+            # Test eager and non eager mode for v2
+            run_eagerly = eager_mode
         if script_mode:
             hook = smd.KerasHook.create_from_json_file()
             opt = hook.wrap_optimizer(opt)
             model.compile(
-                loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"]
+                loss="sparse_categorical_crossentropy",
+                optimizer=opt,
+                metrics=["accuracy"],
+                run_eagerly=run_eagerly,
             )
             history = model.fit(
                 x_train, y_train, batch_size=64, epochs=2, validation_split=0.2, callbacks=[hook]
@@ -122,7 +144,10 @@ def helper_test_keras_v2_json_config(
             test_scores = model.evaluate(x_test, y_test, verbose=2, callbacks=[hook])
         else:
             model.compile(
-                loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"]
+                loss="sparse_categorical_crossentropy",
+                optimizer=opt,
+                metrics=["accuracy"],
+                run_eagerly=run_eagerly,
             )
             history = model.fit(x_train, y_train, epochs=2, batch_size=64, validation_split=0.2)
             test_scores = model.evaluate(x_test, y_test, verbose=2)
@@ -134,7 +159,9 @@ def helper_test_keras_v2_json_config(
         trial = smd.create_trial(path=sim.out_dir)
         assert len(trial.steps()) > 0, "Nothing saved at any step."
         assert len(trial.tensor_names()) > 0, "Tensors were not saved."
-        if not eager_mode:
+        if not eager_mode and is_tf_2_3() is False:
+            # Gradients are currently not saved in ZCC mode with AWS TF 2.3.0
+            # and smdebug 0.9
             assert len(trial.tensor_names(collection="gradients")) > 0
         assert len(trial.tensor_names(collection="weights")) > 0
         assert len(trial.tensor_names(collection="losses")) > 0