You work for a large healthcare diagnostics company. You are on the machine learning team responsible for predicting various anomalies in blood samples. You have data samples from all of the corporation’s many testing facilities across the country. You have performed feature engineering and data cleaning on your dataset. You have also written the python code to split your dataset into training and test datasets. You are now ready to train your model for the first time.
You have written the following python code in your SageMaker jupyter notebook:
import sagemaker
from sagemaker.amazon.amazon_estimator import get_image_uri
from sagemaker import get_execution_role
container = get_image_uri(boto3.Session().region_name, `xgboost`)
role = get_execution_role()
s3_train = `s3://{}/{}/{}`.format(bucket, prefix, `train`)
s3_validation = `s3://{}/{}/{}`.format(bucket, prefix, `validation`)
s3_output = `s3://{}/{}/{}`.format(bucket, prefix, xgb_output)
xgb_model = sagemaker.estimator.Estimator(container,
 role,
 train_instance_count=1,
 train_instance_type=`ml.m4.xlarge`,
 train_volume_size = 5,
 output_path=s3_output,
 sagemaker_session=sagemaker.Session())
xgb_model.set_hyperparameters(max_depth = 2,
eta = 2,
gamma = 2,
min_child_weight = 2,
silent = 0,
objective = "multi:softmax",
num_class = 10,
num_round = 10)
train_channel = sagemaker.session.s3_input(s3_train, content_type=`text/csv`)
valid_channel = sagemaker.session.s3_input(s3_validation, content_type=`text/csv`)
data_channels = {`train`: train_channel, `validation`: valid_channel}
xgb_model.fit(inputs=data_channels,logs=True)
When you attempt to run this code in your SageMaker jupyter notebook, it fails. You check the CloudWatch logs and find this error message:
AlgorithmError: u`2` is not valid under any of the given
schemas\n\nFailed validating u`oneOf` in
schema[u`properties`][u`feature_dim`]:\n{u`oneOf`:
[{u`pattern`: u`^([0]\.[0-9])$`, u`type`: u`string`},\n{u`minimum`: 0, u`type`: u`integer`}]}\
What is the cause of your error?

Answer options:

A.You have used an invalid hyperparameter.
B.You have used an invalid hyperparameter value.
C.You have used an invalid train content_type.
D.You have used an invalid objective.

Answer: B
Option A is incorrect. If you had specified an invalid hyperparameter, you would get an error such as:
ERROR 139623806805824 train.py:48]
Additional properties are not allowed (u`min_child_weigh` was
unexpected) 
Option B is correct. You specified the value of 2 for the eta hyperparameter, but the valid range for this hyperparameter for the XGBoost algorithm is float range: [0,1]
Option C is incorrect. The valid content types for the XGBoost algorithm are text/libsvm (default) or text/csv. You have used text/csv, so your content type is valid.
Option D is incorrect. The objective multi:softmax is a valid setting for the XGBoost algorithm.
Reference:
Please see the Amazon SageMaker developer guide titled Logs for Built-in Algorithms, the Amazon SageMaker developer guide titled XGBoost Hyperparameters, and the XGBoost Parameters GitHub page (especially the Learning Task Parameters section)