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SAA-C03 · Question #313

A machine learning (ML) team is building an application that uses data that is in an Amazon S3 bucket. The ML team needs a storage solution for its model training workflow on AWS. The ML team…

The correct answer is C. Use Amazon FSx for Lustre to provide high-performance storage. Store training datasets in. Amazon FSx for Lustre is a high-performance file system optimized for fast processing of workloads such as machine learning, high-performance computing (HPC), and video processing. It integrates natively with Amazon S3, allowing you to: Access S3 Data: FSx for Lustre can be…

Submitted by manish99· Mar 4, 2026Design High-Performing Architectures

Question

A machine learning (ML) team is building an application that uses data that is in an Amazon S3 bucket. The ML team needs a storage solution for its model training workflow on AWS. The ML team requires high-performance storage that supports frequent access to training datasets. The storage solution must integrate natively with Amazon S3. Which solution will meet these requirements with the LEAST operational overhead?

Options

  • AUse Amazon Elastic Block Store (Amazon EBS) volumes to provide high-performance storage.
  • BUse Amazon EC2 ML instances to provide high-performance storage. Store training data on
  • CUse Amazon FSx for Lustre to provide high-performance storage. Store training datasets in
  • DUse Amazon EMR to provide high-performance storage. Store training datasets in Amazon S3

How the community answered

(34 responses)
  • A
    9% (3)
  • B
    15% (5)
  • C
    71% (24)
  • D
    6% (2)

Explanation

Amazon FSx for Lustre is a high-performance file system optimized for fast processing of workloads such as machine learning, high-performance computing (HPC), and video processing. It integrates natively with Amazon S3, allowing you to: Access S3 Data: FSx for Lustre can be linked to an S3 bucket, presenting S3 objects as files in the file system. High Performance: It provides sub-millisecond latencies, high throughput, and millions of IOPS, which are ideal for ML workloads.Amazon Web Services, Inc. Minimal Operational Overhead: Being a fully managed service, it reduces the complexity of setting up and managing high-performance file systems.

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