Get Latest Mar-2023 Conduct effective penetration tests using VCEDumps HPE2-N69 exam [Q23-Q45]

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Get Latest [Mar-2023] Conduct effective penetration tests using VCEDumps HPE2-N69

Penetration testers simulate HPE2-N69 exam PDF

NO.23 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.

NO.24 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

NO.25 An HPE Machine Learning Development Environment cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?

Training

GPU Jupyter Notebook

Validation

CPU-only Jupyter Notebook

NO.26 The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources. What can the engineer do in the experiment config file to help support this goal?

Under “searcher,” set “max_concurrent_trails” to cap the number of trials run at once by this experiment.

Under “searcher,” set “divisor- to 2 to reduce the share of the resource slots that the experiment receives.

Set the “scheduling_unit” to cap the number of resource slots used at once by this experiment.

Under “resources.- set ‘priority to I to reduce the share of the resource slots mat the experiment receives.

NO.27 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?

Set the weight to a higher than default value.

Set the weight to a lower than default value.

Set the priority to a lower than default value.

Set the priority to a higher than default value.

NO.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)

NO.29 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?

Set the weight to a higher than default value.

Set the weight to a lower than default value.

Set the priority to a lower than default value.

Set the priority to a higher than default value.

NO.30 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?

NO.31 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.

NO.32 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

NO.33 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

NO.34 An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO). What experiment config fields configure this behavior?

profiling: enabled: false

hyperparameters; optimizer:none

searcher: name: single

resources: slots_per_trial: 1

NO.35 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

NO.36 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.

NO.37 At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?

Any of the agent’s in a compute pool

A virtual one assigned to the cluster

The conductor’s

Any of the agent’s in an aux pool

NO.38 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

NO.39 What is one key target vertical (or HPE Machine Learning Development solutions?

Hospitality

K-12education

Retail

Manufacturing

NO.40 An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO). What experiment config fields configure this behavior?

profiling: enabled: false

hyperparameters; optimizer:none

searcher: name: single

resources: slots_per_trial: 1

NO.41 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

NO.42 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

NO.43 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.

NO.44 What is the role of a hidden layer in an artificial neural network (ANN)?

It is responsible for passively reformatting data for use in the ANN.

It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.

It receives and weighs inputs from the preceding layer and produces outputs for the next layer.

It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.

NO.45 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.

HP HPE2-N69 Exam Syllabus Topics:

Topic	Details
Topic 1	Size HPE Machine Learning Development Environment and System solutions Understand machine learning (ML) and deep learning (DL) fundamentals
Topic 2	Describe the HPE Machine Learning Development Environment software architecture and deployment options Have a conversation with customers about machine learning (ML) and deep learning (DL)
Topic 3	Demonstrate and explain how to use HPE Machine Learning Development Environment Describe the architecture for HPE Machine Learning Development solutions
Topic 4	Explain how HPE Machine Learning Development Environment helps customers surmount their challenges Run a proof of concept (PoC)
Topic 5	Explain how the Machine Learning Development Environment uses resources and schedules workloads Understand the challenges customers face in training DL models
Topic 6	Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment Describe how HPE Machine Learning Development Environment fits in the market

Tested Material Used To HPE2-N69 Test Engine: https://www.vcedumps.com/HPE2-N69-examcollection.html

Get Latest Mar-2023 Conduct effective penetration tests using VCEDumps HPE2-N69 exam [Q23-Q45]

HP HPE2-N69 Exam Syllabus Topics:

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