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HP HPE2-N69 Practice Test Questions Answers

Exam Code: HPE2-N69 (Updated 40 Q&As with Explanation)
 Exam Name: Using HPE AI and Machine Learning
 Last Update: 29-Aug-2025
 Demo:  Download Demo

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Questions Include:

  • Single Choice: 40 Q&A's

    • HPE2-N69 Overview

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    HPE2-N69 Questions and Answers

    Question # 1

    Where does TensorFlow fit in the ML/DL Lifecycle?

    A.

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

    B.

    it provides pipelines to manage the complete lifecycle.

    C.

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

    D.

    It adds system and GPU monitoring to the training process.

    Question # 2

    What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

    A.

    it downloads datasets for training.

    B.

    It uploads model checkpoints.

    C.

    It validates trained models.

    D.

    It ensures experiment metadata is stored.

    Question # 3

    You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?

    A.

    The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.

    B.

    The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.

    C.

    The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.

    D.

    The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.

    Question # 4

    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?

    A.

    Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.

    B.

    Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.

    C.

    Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.

    D.

    Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.

    Question # 5

    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.

    Re-enter the command with "-m" in which is the code filename.

    B.

    Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.

    C.

    Re-enter the command with a period (.) at the end.

    D.

    Make sure that you have already logged into the cluster with the "det login’’ command.

    Free HPE2-N69 HP Exam Questions 6 to 17
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