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HP HPE2-N69 Practice Test - Questions Answers, Page 4

Question list

List of questions

Question 31

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Where does TensorFlow fit in the ML/DL Lifecycle?

Question 32

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You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster. Wh

Question 33

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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

Question 34

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What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

Question 35

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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 prior

Question 36

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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?

Question 37

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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?

Question 38

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What distinguishes deep learning (DL) from other forms of machine learning (ML)?

Question 39

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What are the mechanics of now a model trains?

Question 40

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A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

Question 31

Where does TensorFlow fit in the ML/DL Lifecycle?

it helps engineers use a language like Python to code and trail DL models.

it provides pipelines to manage the complete lifecycle.

It is primarily used to transport trained models to a deployment environment.

It adds system and GPU monitoring to the training process.

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You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster. What software must be installed on the machine before you run that command?

Kubernetes

PyTorch

Terralorm

Docker

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Question 33

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

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Question 34

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.

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Question 35

• 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.

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Question 36

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

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Question 37

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

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Question 38

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

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Question 39

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

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Question 40

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.

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HP HPE2-N69 Practice Test - Questions Answers, Page 4

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Question 31

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Question 32

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Question 33

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Question 35

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Question 36

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Question 37

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Question 38

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Question 39

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