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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 78 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 78
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 ТВ of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

  • A. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
  • B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
  • C. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  • D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
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Suggested Answer: A 🗳️
by GiorgioGss at Nov. 28, 2024, 5:33 p.m.
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Saransundar
3 months ago
Selected Answer: A
https://docs.netapp.com/us-en/netapp-solutions/ai/mlops_fsxn_sagemaker_integration_training.html#introduction Option A would work since you can mount FSx for ONTAP directly to SageMaker, enabling fast access to the 6 TB data in the same VPC without extra steps
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