HP Exam HPE2-N69 Topic 7 Question 37 Discussion

Actual exam question for HP's HPE2-N69 exam

Question #: 37
Topic #: 7

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?

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

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

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

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

Show Suggested Answer

Suggested Answer: D

Before running the 'del deploy' command to set up the cluster, you must first install Docker on the machine. Docker is a containerization platform that is used to run applications in an isolated environment. It is necessary to have Docker installed before running the 'del deploy' command to set up the cluster for the open source Determined AI on a local machine.

by Reyes at Jul 01, 2024, 05:38 PM

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Leota

1 years ago

Docker, my dudes. It's like the duct tape of the tech world - it can fix everything, including your Determined AI setup. Just don't try to use it to fix a leaky faucet, that's where it draws the line.

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Darrel

12 months ago

D) Docker

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Kiera

12 months ago

C) Terralorm

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Wilson

12 months ago

B) PyTorch

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Kenda

12 months ago

A) Kubernetes

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Layla

1 years ago

PyTorch? Hmm, I think that's more for the actual machine learning models, not the deployment infrastructure. Docker is the way to go!

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Latosha

1 years ago

I agree with Nadine, Docker is necessary for the setup.

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Lenna

1 years ago

Kubernetes? Well, if you want to go all-in on the cloud-native approach, I guess that could work. But Docker seems like the more straightforward option here.

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