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Exam AWS Certified Machine Learning - Specialty All Questions

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Exam AWS Certified Machine Learning - Specialty topic 1 question 331 discussion

A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR).

A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model.

Which solution will meet these requirements MOST cost-effectively?

  • A. Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances.
  • B. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances.
  • C. Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances.
  • D. Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.
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Suggested Answer: B 🗳️

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Chosen Answer:
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vkbajoria
7 months, 1 week ago
Selected Answer: B
B seems to be most cost effective
upvoted 1 times
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butaman
7 months, 3 weeks ago
Selected Answer: B
The most cost-effective solution is Option B: Use AWS Batch with Amazon EC2 Spot Instances and Amazon FSx for Lustre. This approach leverages the efficiency of AWS Batch, the cost benefits of Spot Instances, and the high-performance of FSx for Lustre, making it ideal for scoring a batch model. However, Spot Instances can be interrupted, so they’re best for flexible workloads.
upvoted 1 times
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AIWave
7 months, 3 weeks ago
Selected Answer: B
AWS Batch managed Amazon EC2 Spot Instances - cost effective! FSx for Lustre - large volume (2 TB) data
upvoted 2 times
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