A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array , which appears to be negatively affecting the speed of the training. What should the Specialist do to optimize the data for training on SageMaker?
A) Use the SageMaker batch transform feature to transform the training data into a DataFrame.
B) Use AWS Glue to compress the data into the Apache Parquet format.
C) Transform the dataset into the RecordIO protobuf format. Transform the dataset into the RecordIO protobuf format.
D) Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

Question

Quizplus · Accepted Answer

Transforming the dataset into the RecordIO protobuf format can optimize the data for training on SageMaker, as it is a more efficient format for reading and processing large datasets compared to CSV or numpy.array.

A Machine Learning Specialist Is Preparing Data for Training on Amazon