documentation: add security note for the estimator hyperparameter arg

src/sagemaker/estimator.py

@@ -423,6 +423,13 @@ def __init__(
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]):
                 A dictionary containing the hyperparameters to
                 initialize this estimator with. (Default: None).
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             container_log_level (int or PipelineVariable): The log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.
@@ -2462,6 +2469,13 @@ def __init__(
                 using the default AWS configuration chain.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]):
                 Dictionary containing the hyperparameters to initialize this estimator with.
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): List of tags for
                 labeling a training job. For more, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
@@ -2939,6 +2953,13 @@ def __init__(
                 SageMaker. For convenience, this accepts other types for keys
                 and values, but ``str()`` will be called to convert them before
                 training.
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             container_log_level (int or PipelineVariable): Log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.