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Exam AWS Certified Machine Learning - Specialty topic 1 question 337 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 337
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model's hyperparameters. The ML engineer notices that the tuning jobs take a long time to run. The tuning jobs continue even when the jobs are not significantly improving against the objective metric.

The ML engineer needs the training jobs to optimize the hyperparameters more quickly.

How should the ML engineer configure the SageMaker AMT data types to meet these requirements?

  • A. Set Strategy to the Bayesian value.
  • B. Set RetryStrategy to a value of 1.
  • C. Set ParameterRanges to the narrow range Inferred from previous hyperparameter jobs.
  • D. Set TrainingJobEarlyStoppingType to the AUTO value.
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Suggested Answer: D 🗳️
by GS_77 at Aug. 30, 2024, 7:43 p.m.
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VerRi
6 months, 1 week ago
Selected Answer: D
stop the job when it is not significantly improving the objective metric
upvoted 1 times
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Tkhan1
7 months, 1 week ago
Selected Answer: D
If you are using the AWS SDK for Python (Boto3), set the TrainingJobEarlyStoppingType field of the HyperParameterTuningJobConfig object that you use to configure the tuning job to AUTO. https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-early-stopping.html
upvoted 2 times
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aragon_saa
7 months, 4 weeks ago
Selected Answer: A
Answer is A
upvoted 1 times
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GS_77
7 months, 4 weeks ago
Selected Answer: D
Set TrainingJobEarlyStoppingType to the AUTO value
upvoted 2 times
...
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