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Exam AWS Certified Machine Learning - Specialty topic 1 question 120 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 120
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

  • A. Redeploy the model as a batch transform job on an M5 instance.
  • B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
  • C. Redeploy the model on a P3dn instance.
  • D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
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Suggested Answer: B 🗳️
by [deleted] at Feb. 5, 2021, 7:47 p.m.
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Highly Voted 3 years, 6 months ago
B is correct. Redeploy with CPU and add elastic inference to reduce costs. See: https://aws.amazon.com/machine-learning/elastic-inference/
upvoted 25 times
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Togy
Most Recent 2 weeks, 6 days ago
Selected Answer: D
The Amazon EC2 M5 instance family is designed for general-purpose workloads, and they are CPU-optimized. Therefore, M5 instances do not come with GPUs. The only option that talks about optmising the use of already provisoned resources is option D, So that must be the answer.
upvoted 1 times
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MultiCloudIronMan
6 months, 3 weeks ago
Selected Answer: B
This solution allows you to use a more cost-effective instance type while leveraging Elastic Inference to provide the necessary GPU acceleration
upvoted 1 times
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GS_77
7 months, 2 weeks ago
Selected Answer: C
redeploying the model on a P3dn instance is the best approach to ensure the provisioned GPU resources are being utilized effectively.
upvoted 2 times
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AIWave
1 year, 1 month ago
My vote is B Elastic inference - provides cheper accelration that full GPU - works with M class machines - Works with Tensorflow, MXNet, pytorch, image classification and object detection algorithms
upvoted 1 times
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sukye
1 year, 5 months ago
Elastic Inference has been depreciated since Apr 2023.
upvoted 3 times
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Mickey321
1 year, 7 months ago
Selected Answer: B
can reduce the cost and improve the resource utilization of your model, as Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to run inference workloads with a fraction of the compute resources. You can also choose the right amount of inference acceleration that suits your needs, and scale it up or down as needed.
upvoted 2 times
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AjoseO
2 years, 2 months ago
Selected Answer: B
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to EC2 and Sagemaker instances, to reduce the cost of running deep learning inference. You can choose any CPU instance that is best suited to the overall compute and memory needs of your application, and then separately configure the right amount of GPU-powered inference acceleration. This would allow you to efficiently utilize resources and reduce costs.
upvoted 3 times
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Peeking
2 years, 4 months ago
Selected Answer: B
Elastic inference enables GPU only when load increases. With 50% utilisation there is no need to deploy P3 as the base inference machine.
upvoted 1 times
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ystotest
2 years, 4 months ago
Selected Answer: B
Agreed with B
upvoted 1 times
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Shailendraa
2 years, 7 months ago
12-sep exam
upvoted 2 times
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SriAkula
3 years, 1 month ago
Answer: B Explanation: https://aws.amazon.com/machine-learning/elastic-inference/
upvoted 2 times
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mahmoudai
3 years, 6 months ago
B: production mostly needs CPU with EI rather than GPU machines
upvoted 1 times
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mona_mansour
3 years, 6 months ago
B..>Amazon Elastic Inference (EI) is a resource you can attach to your Amazon EC2 CPU instances to accelerate your deep learning (DL) inference workloads. Amazon EI accelerators come in multiple sizes and are a cost-effective method to build intelligent capabilities into applications running on Amazon EC2 instances.
upvoted 3 times
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Vita_Rasta84444
3 years, 6 months ago
B is correct
upvoted 1 times
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C (25%)
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