A Machine Learning Specialist Is Designing a Scalable Data Storage

question 78

Multiple Choice

A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords. Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

• A) Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
• B) Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
• C) Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
• D) Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

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Definitions:

Mean

The arithmetic average of a dataset, found by adding all the numbers in the set and dividing by the count of numbers.

Standard Error

A statistical measure that describes the accuracy with which a sample distribution represents a population using the standard deviation.

Mean

The mean of a series of numbers, determined by dividing their total sum by the quantity of numbers in the series.

Confidence Interval

A range of values derived from sample statistics that is likely to contain the true population parameter with a given level of confidence.