You work as a machine learning specialist for a polling research company. You have national polling data for the last 10 presidential elections that you have engineered, randomized, partitioned into various training and test datasets, and stored on S3. You have selected a SageMaker built-in algorithm to use for your model. Your training datasets are very large. As you repeatedly run your training job with different large datasets, you find your training takes a very long time.
How can you improve the performance of your training runs? (Select TWO)

Answer options:

A.Use the protobuf recordIO format.
B.Convert your data to XML and use file mode to load your data to the EBS training instance volumes.
C.Use pipe mode to stream the training data directly to your EBS training instance volumes.
D.Convert your data to CSV and use file mode to load your data to the EBS training instance volumes.
E.Change your Elastic Inference accelerator type to a larger instance type.

Answers: A, C
Option A is correct. The protobuf recordIO format, used for training data, is the optimal way to load data into your model for training. (See the Amazon SageMaker developer guide titled Common Data Formats for Training)
Option B is incorrect. XML is not a supported data format for training in SageMaker. (See the Amazon SageMaker developer guide titled Common Data Formats for Training)
Option C is correct. When you use the protobuf recordIO format, you can also take advantage of pipe mode when training your model. Pipe mode, used together with the protobuf recordIO format, gives you the best data load performance by streaming your data directly from S3 to your EBS volumes used by your training instance. (See the Amazon SageMaker developer guide titled Common Data Formats for Training)
Option D is incorrect. When you use the CSV format and file mode, all of your data is loaded from S3 to the EBS volumes used by your training instance. This is much less efficient from a performance perspective than streaming the training data directly from S3 to your EBS volumes used by your training instance. (See the Amazon SageMaker developer guide titled Common Data Formats for Training)
Option E is incorrect. Elastic Inference is used to speed up the throughput of retrieving real-time inferences from models deployed as SageMaker hosted models. Elastic Inference accelerators accelerate your inference calls; they aren’t used while training. (See the Amazon SageMaker developer guide titled Amazon SageMaker Elastic Inference (EI))
Reference:
Please see the Amazon SageMaker developer guide titled Common Data Formats for Built-in Algorithms and the AWS FAQ titled Amazon Elastic Inference FAQs.