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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 74 discussion

A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

  • A. AWS Secrets Manager
  • B. AWS CodeStar
  • C. Amazon ECR
  • D. Amazon ECS
  • E. Amazon S3
Show Suggested Answer Hide Answer
Suggested Answer: CE 🗳️

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Paul_NoName
Highly Voted 3 years, 7 months ago
CE is the right answer. ECR uses ECS internally while using SGM.
upvoted 11 times
[Removed]
3 years, 6 months ago
CE based on criteria and this documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-mkt-create-model-package.html "For Location of inference image, type the path to the image that contains your inference code. The image must be stored as a Docker container in Amazon ECR. For Location of model data artifacts, type the location in S3 where your model artifacts are stored."
upvoted 6 times
ZSun
2 years ago
the answer is correct but the explanation is completely wrong. The question is about how to create your own algorithm using container, not "put the inference in market" (which is your resource link). the right citation should be "Adapting your own training container": create s3 to store model articraft, and push code to ECR.
upvoted 3 times
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SophieSu
Highly Voted 3 years, 6 months ago
CE IS THE CORRECT ANSWER 100%
upvoted 5 times
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MultiCloudIronMan
Most Recent 7 months, 1 week ago
Selected Answer: CE
Amazon ECR (Option C): Amazon Elastic Container Registry (ECR) is used to store, manage, and deploy Docker container images. The team can package their custom algorithm code into a Docker container and store it in Amazon ECR1. Amazon S3 (Option E): Amazon Simple Storage Service (S3) is used to store external assets and data. The team can store the algorithm-specific parameters and any other required data in Amazon S3
upvoted 1 times
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Mickey321
1 year, 8 months ago
Selected Answer: CE
Amazon ECR is a fully managed container registry service that allows users to store, manage, and deploy Docker container images. Amazon SageMaker supports using custom Docker images for training and inference, which can contain the user’s own training algorithm and any external assets or dependenciesAd1. The user can push their Docker image to Amazon ECR and then reference it in their Amazon SageMaker training job configurationAd1.
upvoted 1 times
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jackzhao
2 years, 1 month ago
CE is correct!
upvoted 1 times
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Valcilio
2 years, 1 month ago
ECR for the code, S3 for the parameters!
upvoted 1 times
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Valcilio
2 years, 1 month ago
Selected Answer: CE
C contain the algorithm's image and E contain algorithm's parameters.
upvoted 2 times
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randomnamer
3 years, 6 months ago
The location of the model artifacts. Model artifacts can either be packaged in the same Docker container as the inference code or stored in Amazon S3. Not so sure.
upvoted 1 times
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cnethers
3 years, 6 months ago
https://aws.amazon.com/blogs/machine-learning/bringing-your-own-custom-container-image-to-amazon-sagemaker-studio-notebooks/ If you wish to use your private VPC to securely bring your custom container, you also need the following: A VPC with a private subnet VPC endpoints for the following services: Amazon Simple Storage Service (Amazon S3) Amazon SageMaker Amazon ECR AWS Security Token Service (AWS STS) CodeBuild for building Docker containers Answer C+E
upvoted 3 times
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ahquiceno
3 years, 7 months ago
For me CD. needs storage and create a custom docker using ECR to store it.
upvoted 1 times
ahquiceno
3 years, 6 months ago
Sorry, CE is correct.
upvoted 1 times
gcpwhiz
3 years, 6 months ago
Sagemaker will spin up the instances needed with the right image. No need to use ECS. CE is right
upvoted 1 times
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A (35%)
C (25%)
B (20%)
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