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HPE2-N69 PDF + Testing Engine



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  • Exam Name: Using HPE AI and Machine Learning
  • Last Update: 31-Jan-2023
  • Questions and Answers: 40
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HPE2-N69 Questions and Answers

Question # 1

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?


Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)






Data Center Bridging (OCB)-enabled Ethernet

Question # 2

A customer mentions that the ML team wants to avoid overfitting models. What does this mean?


The team wants to avoid wasting resources on training models with poorly selected hyperparameters.


The team wants to spend less time on creating the code tor models and more time training models.


The team wants to avoid training models to the point where they perform less well on new data.


The team wants to spend less time figuring out which CPUs are available for training models.

Question # 3

Refer to the exhibit.

HPE2-N69 question answer

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?


Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.


Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.


Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.


Validation loss is metadata that indicates how many updates were lost between the conductor and agents.

Question # 4

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?


Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required


Deploying two HPE Machine Learning Development Environment clusters, one tor each server type


Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs


Establishing multiple compute resource pools on the cluster, one tor servers or each type

Question # 5

You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?


The number of servers in the cluster


The number of agent GPUs


The number of processor cores on agents


The number of processor cores on all servers in the cluster

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