HP HPE2-N69 Dumps – 100% Cover Real Exam Questions (Updated 42 Questions) [Q19-Q35]

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HP HPE2-N69 Dumps – 100% Cover Real Exam Questions (Updated 42 Questions)

Real HPE2-N69 dumps – Real HP dumps PDF

QUESTION 19
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 20
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 21
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 22
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

QUESTION 23
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

QUESTION 24
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 25
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 conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

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

QUESTION 26
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 27
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?

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

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

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

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

QUESTION 28
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 29
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 30
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 31
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?

They run validation and checkpoint workloads.

They run training workloads that do not require GPUs.

They host management software such as the conductor and HPCM.

They run non-distributed training workloads.

QUESTION 32
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 33
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 34
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required

Deploying two HPE Machine Learning Development Environment clusters, one tor each server type

Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs

Establishing multiple compute resource pools on the cluster, one tor servers or each type

QUESTION 35
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