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 HPE2-N69 Dumps with Practice Exam Questions Answers

Questions: 40 questions

Last Update: Jan 28, 2023

HP Certification Exam HPE2-N69 has been designed to measure your skills in handling the technical tasks mentioned in the certification syllabus

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HPE2-N69 Questions and Answers

Question # 1

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


Experiment tracking


Model Inferencing


Distributed training


Premium dedicated support

Question # 2

You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster. What software must be installed on the machine before you run that command?









Question # 3

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


It is responsible for passively reformatting data for use in the ANN.


It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.


It receives and weighs inputs from the preceding layer and produces outputs for the next layer.


It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.

Question # 4

Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment. What can you do?


In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.


Use the "det experiment download -top-n I" command, referencing the experiment ID.


In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.


Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment ID.

Question # 5

Where does TensorFlow fit in the ML/DL Lifecycle?


it helps engineers use a language like Python to code and trail DL models.


it provides pipelines to manage the complete lifecycle.


It is primarily used to transport trained models to a deployment environment.


It adds system and GPU monitoring to the training process.