Warm Start Hyperparameter Tuning Job

[LiY]

I would like to clarify with you with regards to AWS Certified Machine Learning – Specialty Practice Exams >> Bonus Review Mode Set 3 >> Question1 where the correct answer was highlighted as option 4 to enable warm start for its hyperparameter tuning job.

According to the official AWS documentation on SageMaker Automatic Model Tuning – Warm Start: https://docs.aws.<wbr>amazon.com/sagemaker/latest/<wbr>dg/automatic-model-tuning-<wbr>warm-start.html, it stated that “The objective metric used in the new tuning job must be the same as the objective metric used in the parent jobs.” In question 1, it is clearly stated that “the objective metric has been changed to recall.” .

So warm start cannot be used if the objective metric has changed as it contradicts the AWS warm start tuning restrictions. 

AWS Certified Machine Learning Specialty Practice Exams 2025

[Irene-TutorialsDojo]

Hi LiY,

Thank you for reaching out with your observation regarding the question on warm start hyperparameter tuning.

You are correct. According to the official AWS SageMaker AI documentation, a warm start hyperparameter tuning job requires that the objective metric in the new tuning job must be the same as in the previous jobs. Since the scenario states that the objective metric was changed to recall, warm start tuning cannot be used.

Because of this restriction, the best approach in such a case is to start a new hyperparameter tuning job without referencing previous results while continuing to use Bayesian optimization to explore the expanded hyperparameter range efficiently.

We appreciate your attention to detail and for bringing this to our attention. We have updated the question scenario accordingly, and this update will reflect on the portal soon.

If you have any more questions or feedback, please don’t hesitate to contact us.

Best,

Irene @ Tutorials Dojo