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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 70 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 70
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?

  • A. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
  • B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
  • C. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
  • D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.
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Suggested Answer: C 🗳️
by GiorgioGss at Nov. 28, 2024, 8:48 a.m.
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Saransundar
3 months ago
Selected Answer: C
Load is predictable and sustainable with 2 hrs usage pattern; Needs quick response as well; Sagemaker - Provisioned concurrency + Serverless inference will be able to support it. https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html
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GiorgioGss
3 months ago
Selected Answer: C
For only 2h each day it does not worth provisioning resources. Go serverless.
upvoted 2 times
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