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Free HPE2-N69 Exam Braindumps (page: 3)

Page 2 of 11
PDF with 40 Questions

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?

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

Answer(s): A

Explanation:

Validation loss is a metric used to measure how well the model is performing on unseen data. It is calculated by taking the difference between the predicted values and the actual values. The lower the validation loss, the better the model's performance on new data.



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.






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