You work as a machine learning specialist for an eyewear manufacturing plant. There you have used XGBoost to train a model that uses assembly line image data to categorize contact lenses as malformed or correctly formed. You have engineered your data and used CSV as your Training ContentType. You are now ready to deploy your model using the Amazon SageMaker hosting service.
Assuming you used the default configuration settings, which of the following are true statements about your hosted model? (Select THREE)

Answer options:

A.The training instance class is multiple-instance GPU.
B.The algorithm is not parallelizable for distributed training.
C.The training data target value should be in the first column of the CSV with no header.
D.The training data target value should be in the last column of the CSV with no header.
E.The inference data target value should be in the first column of the CSV with no header.
F.The inference CSV data has no label column.
G.The training instance class is CPU.

Answers: C, F, G
Option A is incorrect. The SageMaker XGBoost currently only supports a CPU instance or a single-instance GPU instance type for training. (See the Amazon SageMaker developer guide titled XGBoost Algorithm, particularly the EC2 Instance Recommendation for the XGBoost Algorithm section)
Option B is incorrect. The XGBoost algorithm is parallelizable and therefore can be deployed on multiple CPU instances for distributed training. (See the Amazon SageMaker developer guide titled Common Parameters for Built-in Algorithms)
Option C is correct. From the Amazon SageMaker developer guide titled XGBoost Algorithm, “For CSV training, the algorithm assumes that the target variable is in the first column and that the CSV does not have a header record”.
Option D is incorrect. From the Amazon SageMaker developer guide titled Common Data Formats for Training, “Amazon SageMaker requires that a CSV file doesn`t have a header record and that the target variable is in the first column”.
Option E is incorrect.From the Amazon SageMaker developer guide titled XGBoost Algorithm, “For CSV inference, the algorithm assumes that CSV input does not have the label column”.
Option F is correct.From the Amazon SageMaker developer guide titled XGBoost Algorithm, “For CSV inference, the algorithm assumes that CSV input does not have the label column”.
Option G is correct.The SageMaker XGBoost currently only supports a CPU instance type for training. (See the Amazon SageMaker developer guide titled XGBoost Algorithm, particularly the EC2 Instance Recommendation for the XGBoost Algorithm section)
Reference:
Please see the Amazon SageMaker developer guide titled Deploy a Model on Amazon SageMaker Hosting Services for an overview of the deployment of a SageMaker model.