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HP HPE2-N69 Exam With Confidence Using Practice Dumps

Exam Code:
HPE2-N69
Exam Name:
Using HPE AI and Machine Learning
Certification:
HPE Product Certified - AI and Machine Learning [2022]
Vendor:
HP
Questions:
40
Last Updated:
Jun 15, 2025
Exam Status:
Stable
HP HPE2-N69

HPE2-N69: HPE Product Certified - AI and Machine Learning [2022] Exam 2025 Study Guide Pdf and Test Engine

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Using HPE AI and Machine Learning Questions and Answers

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Question 1

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?

Options:

A.

Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.

B.

Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.

C.

Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.

D.

Validation loss is metadata that indicates how many updates were lost between the conductor and agents.

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Question 2

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?

Options:

A.

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

B.

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

C.

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

D.

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

Question 3

A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.

What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?

Options:

A.

A lack of understanding of the DL model architecture by the NL engineering team

B.

The complexity of adjusting model code to distribute the training process across multiple GPUs

C.

A lack of adequate power and cooling for the GPU-enabled servers

D.

The requirement that the ML team must wait for the IT team to initiate each new training process

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