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Exam AWS Certified Machine Learning - Specialty topic 1 question 321 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 321
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A machine learning (ML) engineer uses Bayesian optimization for a hyperpara meter tuning job in Amazon SageMaker. The ML engineer uses precision as the objective metric.

The ML engineer wants to use recall as the objective metric. The ML engineer also wants to expand the hyperparameter range for a new hyperparameter tuning job. The new hyperparameter range will include the range of the previously performed tuning job.

Which approach will run the new hyperparameter tuning job in the LEAST amount of time?

  • A. Use a warm start hyperparameter tuning job.
  • B. Use a checkpointing hyperparameter tuning job.
  • C. Use the same random seed for the hyperparameter tuning job.
  • D. Use multiple jobs in parallel for the hyperparameter tuning job.
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Suggested Answer: A 🗳️
by AIWave at March 14, 2024, 6:32 a.m.
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Peter_Hsieh
7 months ago
https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html
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vkbajoria
7 months, 2 weeks ago
Selected Answer: A
using warm start allows to reuse the results of previous tune run
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AIWave
7 months, 2 weeks ago
Selected Answer: A
A: Yes - Warm start allows you to reuse the results from a previously performed hyperparameter tuning job B: No - is related to saving intermediate model checkpoints during training C: No - won’t directly impact the tuning job time D: No - would increase computational resources but won’t necessarily reduce time
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