MLS-C01 · Question #263
A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each…
The correct answer is D. Use FastFile mode in SageMaker to stream the files on demand from the S3 buckets. For larger datasets with larger files (more than 50 MB per file), the first option is to try fast file mode, which is more straightforward to use than FSx for Lustre because it doesn't require creating a file system, or connecting to a VPC. Fast file mode is ideal for large…
Question
A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup. Which solution will meet these requirements?
Options
- AUse File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.
- BCreate an Amazon FSx for Lustre file system. Link the file system to the S3 buckets.
- CCreate an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the
- DUse FastFile mode in SageMaker to stream the files on demand from the S3 buckets.
How the community answered
(39 responses)- A3% (1)
- B8% (3)
- C5% (2)
- D85% (33)
Explanation
For larger datasets with larger files (more than 50 MB per file), the first option is to try fast file mode, which is more straightforward to use than FSx for Lustre because it doesn't require creating a file system, or connecting to a VPC. Fast file mode is ideal for large file containers (more than 150 MB), and might also do well with files more than 50 MB. https://docs.aws.amazon.com/sagemaker/latest/dg/model-access-training-data.html#model- access-training-data-best-practices
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