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HPE2-N69 Using HPE AI and Machine Learning

  • HP
  • 5 free sample questions

About this exam

HPE2-N69 is the HP exam Using HPE AI and Machine Learning. Paid practice material for this exam is not available yet, but you can try its free sample questions.

Exam code
HPE2-N69
Vendor
HP
Practice questions
Not available yet
Free sample questions
5
Question bank last updated
Not available yet

Try 2 sample questions

Taken from the free sample set for HPE2-N69.

Question 1

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?

  1. Option A:

    Adjusting how many of the latest and best checkpoints are saved in the experiment config ' s checkpoint storage settings.

  2. Option B:

    Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.

  3. Option C:

    Double-checking that the checkpoint storage location is operating under 90% of total capacity.

  4. Option D:

    Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.

Show answer and explanation

Correct answer: A

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.

Question 2

A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

  1. Option A:

    Set the weight to a higher than default value.

  2. Option B:

    Set the weight to a lower than default value.

  3. Option C:

    Set the priority to a lower than default value.

  4. Option D:

    Set the priority to a higher than default value.

Show answer and explanation

Correct answer: A

Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.

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