HPE2-N69 Exam Fees - Online HPE2-N69 Version

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HP HPE2-N69 is an industry-leading certification exam that focuses on the HP HPE Cray Artificial Intelligence Development Environment. Using HPE Cray AI Development Environment certification exam is designed for individuals who want to validate their skills in implementing and designing the HPE Cray AI Development Environment.

HP HPE2-N69 certification exam is designed to assess the knowledge and skills of individuals in using the HPE Cray AI Development Environment. HPE2-N69 exam is intended for professionals who work with AI and machine learning technologies and are looking to validate their expertise in using HPE Cray AI Development Environment.

HPE2-N69 certification exam covers a wide range of topics related to HPE Cray AI Development Environment, including data preparation, model creation, training, and deployment. HPE2-N69 exam also focuses on the use of tools and techniques for optimizing the performance of machine learning and deep learning models. Additionally, the exam covers best practices for managing and monitoring machine learning and deep learning applications deployed on HPE Cray AI Development Environment.

HPE2-N69 Exam Fees

2023 HPE2-N69: Using HPE Cray AI Development Environment –The Best Exam Fees

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HP Using HPE Cray AI Development Environment Sample Questions (Q36-Q41):

NEW QUESTION # 36
An HPE Machine Learning Development Environment cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?

  • A. CPU-only Jupyter Notebook
  • B. GPU Jupyter Notebook
  • C. Validation
  • D. Training

Answer: A


NEW QUESTION # 37
You are in a directory on your machine with your experiment config file and your model code. You enter this command:
det experiment create myfile.yaml
You receive this error:
det experiment create: error: the following arguments are required: model_def What should you do?

  • A. Make sure that you have already logged into the cluster with the "det login'' command.
  • B. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
  • C. Re-enter the command with "-m" in which is the code filename.
  • D. Re-enter the command with a period (.) at the end.

Answer: B

Explanation:
Make sure that the myfile.yaml tile includes code for a PyTorchTrial or TFKerasTrial class. When creating an experiment with the det experiment create command, you need to specify the model_def parameter to provide the code for the PyTorchTrial or TFKerasTrial class. This code should be specified in the myfile.yaml file, so make sure that the myfile.yaml file includes the code for the model you want to use.


NEW QUESTION # 38
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?

  • A. Complex and time-consuming hyperparameter optimization (HPO)
  • B. Complex and time-consuming data cleansing process
  • C. Time-consuming data collection
  • D. Complex model deployment processes

Answer: A

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.


NEW QUESTION # 39
An ML engineer is running experiments on HPE Machine Learning Development Environment. The engineer notices all of the checkpoints for a trial except one disappear after the trial ends. The engineer wants to Keep more of these checkpoints. What can you recommend?

  • A. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
  • B. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
  • C. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
  • D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.

Answer: B

Explanation:
The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.


NEW QUESTION # 40
What is the role of a hidden layer in an artificial neural network (ANN)?

  • A. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
  • B. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
  • C. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.
  • D. It is responsible for passively reformatting data for use in the ANN.

Answer: A


NEW QUESTION # 41
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