Ace HP HPE2-N69 Certification with Actual Questions Dec 30, 2023 Updated [Q10-Q29]

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Ace HP HPE2-N69 Certification with Actual Questions Dec 30, 2023 Updated

2023 The Most Effective HPE2-N69 with 42 Questions Answers

QUESTION 10
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 11
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.

Implementing HPO manually can be time-consuming and demand a great deal of expertise.

ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

QUESTION 12
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 13
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 14
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 15
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 16
What are the mechanics of now a model trains?

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

Adjusts the model’s parameter weights such that the model can Better perform its tasks

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

Detects Data drift of content drift that might compromise the ML model’s performance

QUESTION 17
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 18
The 10 agents in “my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting for “resources_per_trial?

QUESTION 19
The 10 agents in “my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting tor “resources_per_trial?

QUESTION 20
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?

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

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

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

Make sure that you have already logged into the cluster with the “det login” command.

QUESTION 21
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.

ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.

Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.

Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.

QUESTION 22
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

Automated user provisioning

Pipeline-based data management

Distributed training

Automated hyperparameter optimization (HPO)

QUESTION 23
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 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
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

The team wants to avoid wasting resources on training models with poorly selected hyperparameters.

The team wants to spend less time on creating the code tor models and more time training models.

The team wants to avoid training models to the point where they perform less well on new data.

The team wants to spend less time figuring out which CPUs are available for training models.

QUESTION 26
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?

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

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

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

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

QUESTION 27
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 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
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?

installing the NVIDIA Container Toolkit on your local machine

Manually creating the AWS EC2 instance with a PostgreSQL database

Recording the name of a valid AWS EC2 keypair

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

HP HPE2-N69 certification exam is an essential exam for IT professionals who want to enhance their skills in using HPE Cray AI Development Environment. HPE2-N69 exam is designed to test the candidate’s knowledge and skills in developing and deploying AI applications using HPE Cray AI Development Environment. HPE2-N69 exam consists of 60 multiple-choice questions, and the candidate will have 90 minutes to complete it.

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