A team is running a hyperparameter tuning job on Vertex AI. They notice that many trials are failing with the error 'Resource exhausted: Out of memory'. The training code is correct. What is the best way to address this?

Reduce the batch size in the hyperparameter search space.

Use a smaller machine type for each trial.

Disable hyperparameter tuning and run a single trial.

Increase the number of trials to allow more attempts.

Google CloudProfessional Machine Learning Engineer

Domain 3Objective 2

Training Models PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 7)

Part of the Scaling prototypes into ML models domain, which accounts for ~21% of the PROFESSIONAL-MACHINE-LEARNING-ENGINEER exam.

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A team is running a hyperparameter tuning job on Vertex AI. They notice that many trials are failing with the error 'Resource exhausted: Out of memory'. The training code is correct. What is the best way to address this?
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Training Models PROFESSIONAL-MACHINE-LEARNING-ENGINEER Practice Questions (Page 7)

Questions 31–31

A team is running a hyperparameter tuning job on Vertex AI. They notice that many trials are failing with the error 'Resource exhausted: Out of memory'. The training code is correct. What is the best way to address this?

Keep studying — Scaling prototypes into ML models