HP HPE2-N69 Exam Questions Instant Access

Question 1

Where does TensorFlow fit in the ML/DL Lifecycle?

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

Bit provides pipelines to manage the complete lifecycle.

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

DIt adds system and GPU monitoring to the training process.

Answer : B

TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data ingestion to model training, evaluation, and deployment. It helps engineers use a language like Python to code and train DL models, and it also adds system and GPU monitoring to the training process. Additionally, it can be used to transport trained models to a deployment environment.

Question 2

An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:

* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50

* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I

What happens?

ATrial I is allowed to finish. Then Trial 3 is scheduled.

BTrial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.

CTrial 1 is allowed to finish. Then Trial 2 is scheduled.

DTrial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

Answer : D

Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.

Question 3

You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use "det deploy" to set up the cluster. What is one prerequisite?

Ainstalling the NVIDIA Container Toolkit on your local machine

BManually creating the AWS EC2 instance with a PostgreSQL database

CRecording the name of a valid AWS EC2 keypair

DAdding Amazon Elastic Kubernetes Services (EKS) to your AWS account

Answer : C

In order to use the 'det deploy' command to set up a cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS), you will need to have a valid AWS EC2 keypair. The keypair will authenticate your access to the cluster and allow you to securely access the cluster once it is set up.

Question 4

What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

AIt uses a centralized training architecture that is highly efficient.

BIt helps DL projects complete faster for a faster ROI.

CIt helps companies deploy models and generate revenue.

DIt automatically cleans up data to create better end results.

Answer : B

HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.

Question 5

What are the mechanics of now a model trains?

ADecides which algorithm can best meet the use case for the application in question

BAdjusts the model's parameter weights such that the model can Better perform its tasks

CTests how accurately the model performs on a wide array of real world data

DDetects Data drift of content drift that might compromise the ML model's performance

Answer : B

This is done by running the model through a training loop, where the model is fed data and the parameter weights are adjusted based on the results of the model's performance on the data. For example, if the model is a neural network, the weights of the connections between the neurons are adjusted based on the results of the model's performance on the data. This process is repeated until the model performs better on the data, at which point the model is considered trained.

Question 6

What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

AEnsuring that all experiments receive their fair share of resources

BMinimizing costs in a cloud environment

CEqually distributing utilization across multiple agents

DEnsuring that the highest priority experiments obtain access to more resources

Answer : D

The best fit policy on an HPE Machine Learning Development Environment resource pool ensures that the highest priority experiments obtain access to more resources, while still ensuring that all experiments receive their fair share. This allows you to make the most of your resources and prioritize the experiments that are most important to you.

Question 7

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?

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

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

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

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

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.

HP Using HPE AI and Machine Learning HPE2-N69 Exam Questions