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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 315 discussion

A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset. The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameters that will generate the most accurate model.

Which approach will meet these requirements with the LEAST operational overhead?

  • A. Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm.
  • B. Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.
  • C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
  • D. Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.
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Suggested Answer: C 🗳️

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Peter_Hsieh
5 months, 4 weeks ago
Selected Answer: C
https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html
upvoted 2 times
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vkbajoria
7 months, 1 week ago
Selected Answer: C
automated model Tuning will be the best solution here
upvoted 1 times
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rav009
7 months, 1 week ago
Selected Answer: C
Amazon SageMaker automatic model tuning (AMT) for sure
upvoted 1 times
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AIWave
7 months, 2 weeks ago
Selected Answer: C
Automated model tuning minimizes operational overhead because it automates the entire process of hyperparameter tuning, including setting up and managing the training jobs, tracking performance metrics, and selecting the best model configuration
upvoted 1 times
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F1Fan
7 months, 2 weeks ago
C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
upvoted 1 times
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