Achieve the HPE2-N69 Exam Best Results with Help from HP Certified Experts [Q17-Q37]

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Achieve the HPE2-N69 Exam Best Results with Help from HP Certified Experts

Provide HPE2-N69 Practice Test Engine for Preparation

HP HPE2-N69 exam is designed to test the candidate’s knowledge and skills in using the HPE Cray AI Development Environment. HPE2-N69 exam is primarily intended for IT professionals who want to acquire expertise in using the HPE Cray AI Development Environment to develop, deploy, and maintain artificial intelligence (AI) solutions. HPE2-N69 exam covers a range of topics, including data preparation, model training, model deployment, and model monitoring, among others.

NO.17 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.18 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.19 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.

NO.20 What distinguishes deep learning (DL) from other forms of machine learning (ML)?

Models based on neural networks with interconnected layers of nodes, including multiple hidden layers

Models defined with Apache Spark rather than MapReduce

Models that are trained through unsupervised, rather than supervised, training

Models trained through multiple training processes implemented by different team members

NO.21 What is one key target vertical (or HPE Machine Learning Development solutions?

Hospitality

K-12education

Retail

Manufacturing

NO.22 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.23 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

NO.24 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?

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

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

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

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

NO.25 What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)

Slingshot

InfiniBand

Data Center Bridging (OCB)-enabled Ethernet

NO.26 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

NO.27 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.

NO.28 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.

NO.29 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.30 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.31 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)

NO.32 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.33 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.

NO.34 You are helping a customer start to implement hyper parameter optimization (HPO) with HPE Machine learning Development Environment. An ML engineer is putting together an experiment config file with the desired Adaptive A5HA settings. The engineer asks you questions, such as how many trials will be trained on the max length and what the min length for all trials will be.
What should you explain?

The engineer should run the “det preview-search” command, referencing the experiment config.

The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.

The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.

The engineer should run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.

NO.35 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.36 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

NO.37 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.

Detailed New HPE2-N69 Exam Questions for Concept Clearance: https://www.testsimulate.com/HPE2-N69-study-materials.html

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