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NEW QUESTION 50
An ML engineer is running experiments on HPE Machine Learning Development Environment. The engineer notices all of the checkpoints for a trial except one disappear after the trial ends. The engineer wants to Keep more of these checkpoints. What can you recommend?

A. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.B. Double-checking that the checkpoint storage location is operating under 90% of total capacity.C. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.D. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.

Answer: A

Explanation:
The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.

 

NEW QUESTION 51
Where does TensorFlow fit in the ML/DL Lifecycle?

A. It is primarily used to transport trained models to a deployment environment.B. It adds system and GPU monitoring to the training process.C. it helps engineers use a language like Python to code and trail DL models.D. it provides pipelines to manage the complete lifecycle.

Answer: D

Explanation:
TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data ingestion to model training, evaluation, and deployment. It helps engineers use a language like Python to code and train DL models, and it also adds system and GPU monitoring to the training process. Additionally, it can be used to transport trained models to a deployment environment.

 

NEW QUESTION 52
Where does TensorFlow fit in the ML/DL Lifecycle?

A. It is primarily used to transport trained models to a deployment environment.B. it helps engineers use a language like Python to code and trail DL models.C. It adds system and GPU monitoring to the training process.D. it provides pipelines to manage the complete lifecycle.

Answer: B

 

NEW QUESTION 53
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

A. Distributed trainingB. Pipeline-based data managementC. Automated hyperparameter optimization (HPO)D. Automated user provisioning

Answer: B

 

NEW QUESTION 54
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