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HP Using HPE Cray AI Development Environment Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

  • A. Minimizing costs in a cloud environment
  • B. Ensuring that the highest priority experiments obtain access to more resources
  • C. Equally distributing utilization across multiple agents
  • D. Ensuring that all experiments receive their fair share of resources

Answer: B

Explanation:
The best fit policy on an HPE Machine Learning Development Environment resource pool ensures that the highest priority experiments obtain access to more resources, while still ensuring that all experiments receive their fair share. This allows you to make the most of your resources and prioritize the experiments that are most important to you.


NEW QUESTION # 15
Where does TensorFlow fit in the ML/DL Lifecycle?

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

Answer: B


NEW QUESTION # 16
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

  • A. Pipeline-based data management
  • B. Automated user provisioning
  • C. Distributed training
  • D. Automated hyperparameter optimization (HPO)

Answer: D

Explanation:
One of the main benefits of HPE Machine Learning Development Environment is its ability to automate the process of hyperparameter optimization (HPO). HPO is a process of automatically tuning the hyperparameters of a model during training, which can greatly improve a model's performance. HPE ML DE provides automated HPO, making the process of tuning and optimizing the model much easier and more efficient.


NEW QUESTION # 17
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

  • A. it downloads datasets for training.
  • B. It uploads model checkpoints.
  • C. It validates trained models.
  • D. It ensures experiment metadata is stored.

Answer: D

Explanation:
The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.


NEW QUESTION # 18
You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?

  • A. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.
  • B. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
  • C. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
  • D. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.

Answer: A

Explanation:
The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.


NEW QUESTION # 19
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