diff --git a/src/stepfunctions/steps/sagemaker.py b/src/stepfunctions/steps/sagemaker.py
index 9530478..a3ddd47 100644
--- a/src/stepfunctions/steps/sagemaker.py
+++ b/src/stepfunctions/steps/sagemaker.py
@@ -69,9 +69,10 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
                 * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of
                     :class:`sagemaker.amazon.amazon_estimator.RecordSet` objects,
                     where each instance is a different channel of training data.
-            hyperparameters (dict, optional): Parameters used for training.
-                    Hyperparameters supplied will be merged with the Hyperparameters specified in the estimator.
+            hyperparameters: Parameters used for training.
+                * (dict, optional) - Hyperparameters supplied will be merged with the Hyperparameters specified in the estimator.
                     If there are duplicate entries, the value provided through this property will be used. (Default: Hyperparameters specified in the estimator.)
+                * (Placeholder, optional) - The TrainingStep will use the hyperparameters specified by the Placeholder's value instead of the hyperparameters specified in the estimator.
             mini_batch_size (int): Specify this argument only when estimator is a built-in estimator of an Amazon algorithm. For other estimators, batch size should be specified in the estimator.
             experiment_config (dict, optional): Specify the experiment config for the training. (Default: None)
             wait_for_completion (bool, optional): Boolean value set to `True` if the Task state should wait for the training job to complete before proceeding to the next step in the workflow. Set to `False` if the Task state should submit the training job and proceed to the next step. (default: True)
@@ -127,8 +128,9 @@ def __init__(self, state_id, estimator, job_name, data=None, hyperparameters=Non
             parameters['InputDataConfig'][0]['DataSource']['S3DataSource']['S3Uri.$'] = data_uri
 
         if hyperparameters is not None:
-            if estimator.hyperparameters() is not None:
-                hyperparameters = self.__merge_hyperparameters(hyperparameters, estimator.hyperparameters())
+            if not isinstance(hyperparameters, Placeholder):
+                if estimator.hyperparameters() is not None:
+                    hyperparameters = self.__merge_hyperparameters(hyperparameters, estimator.hyperparameters())
             parameters['HyperParameters'] = hyperparameters
 
         if experiment_config is not None:
diff --git a/tests/unit/test_sagemaker_steps.py b/tests/unit/test_sagemaker_steps.py
index 664a498..02c6083 100644
--- a/tests/unit/test_sagemaker_steps.py
+++ b/tests/unit/test_sagemaker_steps.py
@@ -275,6 +275,7 @@ def test_training_step_creation_with_placeholders(pca_estimator):
     execution_input = ExecutionInput(schema={
         'Data': str,
         'OutputPath': str,
+        'HyperParameters': str
     })
 
     step_input = StepInput(schema={
@@ -292,6 +293,7 @@ def test_training_step_creation_with_placeholders(pca_estimator):
             'TrialComponentDisplayName': 'Training'
         },
         tags=DEFAULT_TAGS,
+        hyperparameters=execution_input['HyperParameters']
     )
     assert step.to_dict() == {
         'Type': 'Task',
@@ -312,13 +314,7 @@ def test_training_step_creation_with_placeholders(pca_estimator):
                 'VolumeSizeInGB': 30
             },
             'RoleArn': EXECUTION_ROLE,
-            'HyperParameters': {
-                'feature_dim': '50000',
-                'num_components': '10',
-                'subtract_mean': 'True',
-                'algorithm_mode': 'randomized',
-                'mini_batch_size': '200'
-            },
+            'HyperParameters.$': "$$.Execution.Input['HyperParameters']",
             'InputDataConfig': [
                 {
                     'ChannelName': 'training',
@@ -344,6 +340,49 @@ def test_training_step_creation_with_placeholders(pca_estimator):
     }
 
 
+@patch('botocore.client.BaseClient._make_api_call', new=mock_boto_api_call)
+@patch.object(boto3.session.Session, 'region_name', 'us-east-1')
+def test_training_step_creation_with_hyperparameters_containing_placeholders(pca_estimator):
+    execution_input = ExecutionInput(schema={
+        'Data': str,
+        'OutputPath': str,
+        'num_components': str,
+        'HyperParamA': str,
+        'HyperParamB': str,
+    })
+
+    step_input = StepInput(schema={
+        'JobName': str,
+    })
+
+    step = TrainingStep('Training',
+        estimator=pca_estimator,
+        job_name=step_input['JobName'],
+        data=execution_input['Data'],
+        output_data_config_path=execution_input['OutputPath'],
+        experiment_config={
+            'ExperimentName': 'pca_experiment',
+            'TrialName': 'pca_trial',
+            'TrialComponentDisplayName': 'Training'
+        },
+        tags=DEFAULT_TAGS,
+        hyperparameters={
+            'num_components': execution_input['num_components'],  # This will overwrite the value that was defined in the pca_estimator
+            'HyperParamA': execution_input['HyperParamA'],
+            'HyperParamB': execution_input['HyperParamB']
+        }
+    )
+    assert step.to_dict()['Parameters']['HyperParameters'] == {
+        'HyperParamA.$': "$$.Execution.Input['HyperParamA']",
+        'HyperParamB.$': "$$.Execution.Input['HyperParamB']",
+        'algorithm_mode': 'randomized',
+        'feature_dim': 50000,
+        'mini_batch_size': 200,
+        'num_components.$': "$$.Execution.Input['num_components']",
+        'subtract_mean': True
+    }
+
+
 @patch('botocore.client.BaseClient._make_api_call', new=mock_boto_api_call)
 @patch.object(boto3.session.Session, 'region_name', 'us-east-1')
 def test_training_step_creation_with_debug_hook(pca_estimator_with_debug_hook):