[Oct 19, 2023] HPE2-N69 certification guide Q&A from Training Expert ValidBraindumps [Q16-Q31]

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[Oct 19, 2023] HPE2-N69 certification guide Q&A from Training Expert ValidBraindumps

HPE2-N69 Certification Overview Latest HPE2-N69 PDF Dumps

HP HPE2-N69 exam is designed to evaluate the skills and knowledge of IT professionals who work with the HPE Cray AI Development Environment. Using HPE Cray AI Development Environment certification is designed to validate the candidate’s ability to design, deploy, and manage HPE Cray AI Development Environment solutions. HPE2-N69 exam is intended for individuals who want to advance their careers in the field of AI development and are interested in working with HPE’s Cray AI Development Environment.

QUESTION 16
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?

Kubernetes

PyTorch

Terralorm

Docker

QUESTION 17
You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?

How long does it currently take for a DL training to run the backward pass?

How much do you understand about building ML and DL models?

How much time do you spend managing the ML infrastructure?

What frustrations do you have with existing ML deployment and differencing solutions?

QUESTION 18
A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS). What is an advantage of streaming the data during a trial, as opposed to downloading the data?

Streaming requires just one bucket, while downloading requires many.

The trial can more quickly start up and begin training the model.

The trial can better separate training and validation data.

Setting up streaming is easier that setting up downloading.

QUESTION 19
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?

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

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

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

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

QUESTION 20
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?

The trial tails, and the ML engineer must restart it manually by re-running the experiment.

The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.

The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.

The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

QUESTION 21
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

Ensuring that all experiments receive their fair share of resources

Minimizing costs in a cloud environment

Equally distributing utilization across multiple agents

Ensuring that the highest priority experiments obtain access to more resources

QUESTION 22
What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

It uses a centralized training architecture that is highly efficient.

It helps DL projects complete faster for a faster ROI.

It helps companies deploy models and generate revenue.

It automatically cleans up data to create better end results.

QUESTION 23
You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?

The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.

The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.

The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.

The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.

QUESTION 24
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?

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

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

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

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

QUESTION 25
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

it downloads datasets for training.

It uploads model checkpoints.

It validates trained models.

It ensures experiment metadata is stored.

QUESTION 26
You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?

The number of servers in the cluster

The number of agent GPUs

The number of processor cores on agents

The number of processor cores on all servers in the cluster

QUESTION 27
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?

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

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

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

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

QUESTION 28
A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?

Time-consuming data collection

Complex model deployment processes

Complex and time-consuming data cleansing process

Complex and time-consuming hyperparameter optimization (HPO)

QUESTION 29
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?

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

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

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

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

QUESTION 30
ML engineers are defining a convolutional neural network (CNN) model bur they are not sure how many filters to use in each convolutional layer. What can help them address this concern?

Using hyperparameter optimization (HPO)

Distributing the training across multiple CPUs

Using a variable learning late

Training the model on multiple epochs

QUESTION 31
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?

HP-UX v11i

Windows Server 2016 or above

Windows 10 or above

Red Hat 7-based Linux

HP HPE2-N69 certification exam is designed for professionals who want to enhance their expertise in using HPE Cray AI Development Environment. HPE2-N69 exam is intended for specialists who work with HPE Cray AI Development Environment and are responsible for developing and deploying AI applications. HPE2-N69 exam tests your knowledge and skills in using the HPE Cray AI Development Environment to develop, train, and deploy AI models.

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