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  1. Home
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  3. HPE2-N69

HP HPE2-N69 Exam (page: 2)
HP Using E AI and Machine Learnin
Updated on: 28-Jul-2025

Viewing Page 2 of 9
HPE2-N69 PDF 40 Questions

An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO).
What experiment config fields configure this behavior?

  1. profiling: enabled: false
  2. hyperparameters; optimizer:none
  3. searcher: name: single
  4. resources: slots_per_trial: 1

Answer(s): B

Explanation:

To train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO), you need to set the "optimizer" field to "none" in the hyperparameters section of the experiment config. This will instruct the ML engine to not use any hyperparameter optimization when training the model.



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

  1. Automated user provisioning
  2. Pipeline-based data management
  3. Distributed training
  4. Automated hyperparameter optimization (HPO)

Answer(s): 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.



A customer has Men expanding its deep learning (DO prefects and is confronting several challenges.
Which of these challenges does HPE Machine Learning Development Environment specifically address?

  1. Time-consuming data collection
  2. Complex model deployment processes
  3. Complex and time-consuming data cleansing process
  4. Complex and time-consuming hyperparameter optimization (HPO)

Answer(s): D

Explanation:

The HPE Machine Learning Development Environment specifically addresses Complex and time- consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.



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.



Viewing Page 2 of 9



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