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Question 212 - MLS-C01 discussion

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A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.

How should the data scientist meet these requirements?

A.
Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
Answers
A.
Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
B.
Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
Answers
B.
Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
C.
Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
Answers
C.
Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
D.
Run a SageMaker training job with an EFS file system as the data source.
Answers
D.
Run a SageMaker training job with an EFS file system as the data source.
Suggested answer: D

Explanation:

The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance.References:

Access Training Data - Amazon SageMaker

Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog

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asked 16/09/2024
Knowledge Mathebula
35 questions
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