HP HPE2-N69 Exam Questions Instant Access

Question 1

You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?

AHP-UX v11i

BWindows Server 2016 or above

CWindows 10 or above

DRed Hat 7-based Linux

Answer : D

The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.

Question 2

What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

Ait downloads datasets for training.

BIt uploads model checkpoints.

CIt validates trained models.

DIt ensures experiment metadata is stored.

Answer : D

The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.

Question 3

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

AThe team wants to avoid wasting resources on training models with poorly selected hyperparameters.

BThe team wants to spend less time on creating the code tor models and more time training models.

CThe team wants to avoid training models to the point where they perform less well on new data.

DThe team wants to spend less time figuring out which CPUs are available for training models.

Answer : C

Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.

Question 4

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

AHospitality

BK-12education

CRetail

DManufacturing

Answer : D

One key target vertical for HPE Machine Learning Development solutions is Manufacturing. Manufacturing businesses are using machine learning to automate processes, reduce costs, and improve safety and quality control. HPE ML solutions provide the tools and technologies to help manufacturers develop and deploy ML models in their production environments, enabling them to optimize and automate their operations.

Question 5

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?

AA lack of understanding of the DL model architecture by the NL engineering team

BThe complexity of adjusting model code to distribute the training process across multiple GPUs

CA lack of adequate power and cooling for the GPU-enabled servers

DThe requirement that the ML team must wait for the IT team to initiate each new training process

Answer : B

The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.

Question 6

You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?

AHow long does it currently take for a DL training to run the backward pass?

BHow much do you understand about building ML and DL models?

CHow much time do you spend managing the ML infrastructure?

DWhat frustrations do you have with existing ML deployment and differencing solutions?

Answer : D

A good discovery question to start a conversation about HPE Machine Learning Development Environment with an IT contact at a customer would be: 'What frustrations do you have with existing ML deployment and differencing solutions?' By understanding the customer's current challenges and frustrations, you can better determine how HPE's ML Development Environment could help to address those needs.

Question 7

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?

ATrial I is allowed to finish. Then Trial 3 is scheduled.

BTrial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.

CTrial 1 is allowed to finish. Then Trial 2 is scheduled.

DTrial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

Answer : D

Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.

HP Using HPE AI and Machine Learning HPE2-N69 Exam Questions