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NEW QUESTION 39
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

  • A. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
  • B. Establishing multiple compute resource pools on the cluster, one tor servers or each type
  • C. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
  • D. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type

Answer: B

 

NEW QUESTION 40
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment.
That GPU fails. What happens next?

  • A. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
  • B. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
  • C. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
  • D. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.

Answer: C

 

NEW QUESTION 41
What distinguishes deep learning (DL) from other forms of machine learning (ML)?

  • A. Models that are trained through unsupervised, rather than supervised, training
  • B. Models trained through multiple training processes implemented by different team members
  • C. Models defined with Apache Spark rather than MapReduce
  • D. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers

Answer: A

 

NEW QUESTION 42
Refer to the exhibit.

You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what “validation loss’ means. What should you respond?

  • A. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
  • B. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
  • C. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.
  • D. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.

Answer: D

 

NEW QUESTION 43
……

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