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Free HPE2-N69 Exam Braindumps (page: 4)

Page 3 of 11
PDF with 40 Questions

You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine.
Which OS Is supported?

  1. HP-UX v11i
  2. Windows Server 2016 or above
  3. Windows 10 or above
  4. Red Hat 7-based Linux

Answer(s): D

Explanation:

The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.



The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources.
What can the engineer do in the experiment config file to help support this goal?

  1. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
  2. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
  3. Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.
  4. Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.

Answer(s): A

Explanation:

The ML engineer can set "maxconcurrenttrials" under "searcher" in the experiment config file to cap the number of trials run at once by this experiment. This will help ensure that the experiment does not take up too large a share of resources, allowing other experiments to also run concurrently.



What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?

  1. Experiment tracking
  2. Model Inferencing
  3. Distributed training
  4. Premium dedicated support

Answer(s): C

Explanation:

The benefit of HPE Machine Learning Development Environment beyond open source Determined AI is Distributed Training. Distributed training allows multiple machines to train a single model in parallel, greatly increasing the speed and efficiency of the training process. HPE ML Development Environment provides tools and support for distributed training, allowing users to make the most of their resources and quickly train their models.



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?

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

Answer(s): D

Explanation:

By establishing multiple compute resource pools on the cluster, you can ensure that the correct servers are used for each experiment, depending on the number of GPUs required. This will help ensure that the experiments are run on the servers with the correct resources without having to manually assign each experiment to the appropriate server.






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