[May 24, 2023] HPE2-N69 certification guide Q&A from Training Expert ValidBraindumps [Q10-Q33]

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[May 24, 2023] HPE2-N69 certification guide Q&A from Training Expert ValidBraindumps

HPE2-N69 Certification Overview Latest HPE2-N69 PDF Dumps

The exam consists of 60 questions, which the candidate must answer in 90 minutes. The questions are multiple-choice and cover a range of topics related to HPE Cray AI Development Environment. The exam is available in English and Japanese, and the passing score is 70%. Candidates who pass the exam will receive the HPE ATP – AI Solutions V2 certification.

NEW QUESTION 10
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.

NEW QUESTION 11
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.

NEW QUESTION 12
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

NEW QUESTION 13
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.

NEW QUESTION 14
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

NEW QUESTION 15
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

NEW QUESTION 16
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.

NEW QUESTION 17
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

NEW QUESTION 18
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.

NEW QUESTION 19
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

NEW QUESTION 20
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.

NEW QUESTION 21
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

NEW QUESTION 22
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

NEW QUESTION 23
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)

NEW QUESTION 24
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

NEW 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.

NEW QUESTION 26
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.

NEW QUESTION 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.

NEW QUESTION 28
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

NEW QUESTION 29
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

NEW QUESTION 30
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.

NEW QUESTION 31
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.

NEW QUESTION 32
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

NEW QUESTION 33
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?