Exam AWS Certified Machine Learning - Specialty topic 1 question 284 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 284
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A company is building custom deep learning models in Amazon SageMaker by using training and inference containers that run on Amazon EC2 instances. The company wants to reduce training costs but does not want to change the current architecture. The SageMaker training job can finish after interruptions. The company can wait days for the results.

Which combination of resources should the company use to meet these requirements MOST cost-effectively? (Choose two.)

A. On-Demand Instances
B. Checkpoints
C. Reserved Instances
D. Incremental training
E. Spot instances

Show Suggested Answer

Suggested Answer: BE 🗳️

by aquanaveen at Dec. 17, 2023, 12:57 p.m.

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prash_vz

Highly Voted 11 months, 1 week ago

Selected Answer: BE

https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html

upvoted 5 times

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vkbajoria

Most Recent 9 months, 3 weeks ago

Selected Answer: BE

to pick up where you left off, checkpointing is important and 'sport instances' to save cost

upvoted 1 times

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Adzz

9 months, 4 weeks ago

Selected Answer: BE

Checkpoints & Spot instances

upvoted 1 times

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taustin2

12 months ago

Selected Answer: BE

Spot instances and checkpoints.

upvoted 2 times

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taustin2

1 year ago

Selected Answer: BE

Spot Instance are cheapest and can be used with Checkpoints.

upvoted 2 times

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aquanaveen

1 year ago

Selected Answer: BE

To meet the requirements of reducing training costs and being cost-effective in an Amazon SageMaker environment, the company should consider the following combination of resources: E. Spot Instances: Spot Instances are spare EC2 instances that are available at a lower cost compared to On-Demand Instances. By using Spot Instances for training, the company can significantly reduce the cost of running SageMaker training jobs. B. Checkpoints: Checkpoints allow the model training process to save the model's current state during training. If the training job is interrupted (e.g., due to a Spot Instance termination), the model can resume from the last saved checkpoint rather than starting from scratch.

upvoted 2 times

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