You work for a power tool manufacturer as a machine learning specialist. You work in the battery-powered power tool division, where your team of machine learning specialists and data scientists has been tasked with building a model that predicts the lifespan of particular models of power tools. You have selected the Linear Learner algorithm on which to build your model. You have cleaned and engineered your features for your training and test data. Your feature engineering transformations convert all feature attributes to integers or real numbers. You have also trained your model and have deployed it to Amazon SageMaker Hosting Services.
Your training dataset has this structure:
| model| power | battery Ah | use pattern | region | country |
For your client application inference requests, how would you structure the body argument for your invoke_endpoint call?

Answer options:

A.A string with this value: “547,3.5,1.5,23.4,2,43,1”
B.A string with this value: “547,3.5,1.5,23.4,2,43”
C.A string with this value: “Quite strike,battery,1.5,frequent,North America,US”
D.An array set to these values: [547,3.5,1.5,23.4,2,43]
E.A list set to these values: [547,3.5,1.5,23.4,2,43]

Answer: B
Option A is incorrect. The Linear Learner algorithm expects either CSV or recordio-protobuf as the inference request content type. For text/csv the value of the body argument for the invoke_endpoint API call should be a string with comma-separated values for each feature. This option has a comma-separated string, but it has 7 values, when you only have 6 features in your data used to train your model.
Option B is correct. The Linear Learner algorithm expects either CSV or recordio-protobuf as the inference request content type. For text/csv the value of the body argument for the invoke_endpoint API call should be a string with comma-separated values for each feature. This option has a comma-separated string, and it has 6 values. You also have 6 features in your data used to train your model, so this inference request is structured correctly.
Option C is incorrect. The Linear Learner algorithm expects either CSV or recordio-protobuf as the inference request content type. Also, any transforms performed on the training data must also be performed on inference request data before attempting to obtain an inference. The body argument in this option has not been transformed in the way your training data was transformed.
Option D is incorrect. The Linear Learner algorithm expects either CSV or recordio-protobuf as the inference request content type. Also, the body argument to the invoke_endpoint should be a string, not an array.
Option E is incorrect. The Linear Learner algorithm expects either CSV or recordio-protobuf as the inference request content type. Also, the body argument to the invoke_endpoint should be a string, not a list.
Reference:
Please see the Amazon SageMaker developer guide titled Train a Model with Amazon SageMaker, the Amazon SageMaker developer guide titled Common Parameters for Built-In Algorithms, and the Amazon SageMaker developer guide titled Common Data Formats for Inference.