You work for an oil refinery company where you are on one of their machine learning teams. Your team is responsible for building models that help the company decide where to place their exploratory drilling teams worldwide. Your team lead has decided to build your model based on the K-Means built-in SageMaker algorithm. The team lead has tasked you with providing metric visualization charts for the training runs of your team’s model.
How would you go about visualizing the training metrics? (Select TWO)

Answer options:

A.In your SageMaker jupyter notebook, using the SageMaker python module called pandas.analytics, import TrainingAnalytics .
B.In your SageMaker jupyter notebook, using the SageMaker python module called sagemaker.analytics, import TrainingAnalytics.
C.In your SageMaker jupyter notebook, using the SageMaker python module called sagemaker.analytics, import TrainingJobAnalytics.
D.In your SageMaker jupyter notebook, using the SageMaker python module called pandas.analytics, import TrainingJobAnalytics.
E.Set one of the metric names to test:cross_entropy’
F.Set one of the metric names to test:msd’

Answers: C, F
Option A is incorrect. You use the SageMaker python module called sagemaker.analytics (not pandas.analytics) from which you import TrainingJobAnalytics (not TrainingAnalytics) to gain access to the python methods that allow you to visualize your metrics in charts.
Option B is incorrect. You use the SageMaker python module called sagemaker.analytics from which you import TrainingJobAnalytics (not TrainingAnalytics) to gain access to the python methods that allow you to visualize your metrics in charts.
Option C is correct.You use the SageMaker python module called sagemaker.analytics from which you import TrainingJobAnalytics to gain access to the python methods that allow you to visualize your metrics in charts.
Option D is incorrect. You use the SageMaker python module called sagemaker.analytics (not pandas.analytics) from which you import TrainingJobAnalytics to gain access to the python methods that allow you to visualize your metrics in charts.
Option E is incorrect. To set the metric name that you wish to visualize, you need to give a valid metric for the algorithm you are training. The test:cross_entropy metric is not valid for a K-Means training run.
Option F is correct. To set the metric name that you wish to visualize, you need to give a valid metric for the algorithm you are training. The test:msd metric is one of the two valid for a K-Means training run. The other valid metric for K-Means is test:ssd.
Reference:
Please see the AWS Machine Learning Blog titled Easily monitor and visualize metrics while training models on Amazon SageMaker, and the Amazon SageMaker developer guide titled Tune a K-Means model.