[Q24-Q46] HPE2-N69 Certification - The Ultimate Guide [Updated 2023]

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HPE2-N69 Certification – The Ultimate Guide [Updated 2023]

HPE2-N69 Practice Exam and Study Guides – Verified By Real4exams

QUESTION 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

QUESTION 25
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 26
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

QUESTION 27
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 28
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 29
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.

QUESTION 30
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

QUESTION 31
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

QUESTION 32
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 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

QUESTION 34
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 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

QUESTION 36
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.

QUESTION 37
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 38
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.

QUESTION 39
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 40
What is one key target vertical (or HPE Machine Learning Development solutions?

Hospitality

K-12education

Retail

Manufacturing

QUESTION 41
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 42
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 43
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 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.

QUESTION 45
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 46
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