HP HPE2-N69 Exam Questions Instant Access

Question 1

You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?

AThe number of servers in the cluster

BThe number of agent GPUs

CThe number of processor cores on agents

DThe number of processor cores on all servers in the cluster

Answer : D

The license count for the HPE Machine Learning Development Environment solution would be based on the number of processor cores on all servers in the cluster. This includes all servers in the cluster, regardless of whether they are running agents or not. Each processor core in the cluster requires a license and these licenses can be purchased in packs of 2, 4, 8, and 16.

Question 2

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

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

BModels defined with Apache Spark rather than MapReduce

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

DModels trained through multiple training processes implemented by different team members

Answer : A

Models based on neural networks with interconnected layers of nodes, including multiple hidden layers. Deep learning (DL) is a type of machine learning (ML) that uses models based on neural networks with interconnected layers of nodes, including multiple hidden layers. This is what distinguishes it from other forms of ML, which typically use simpler models with fewer layers. The multiple layers of DL models enable them to learn complex patterns and features from the data, allowing for more accurate and powerful predictions.

Question 3

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?

AThe solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.

BThe solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.

CThe conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.

DThe solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.

Answer : D

The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.

Question 4

What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

AIt uses a centralized training architecture that is highly efficient.

BIt helps DL projects complete faster for a faster ROI.

CIt helps companies deploy models and generate revenue.

DIt automatically cleans up data to create better end results.

Answer : B

HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.

Question 5

A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?

AThe trial tails, and the ML engineer must restart it manually by re-running the experiment.

BThe concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.

CThe conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

DThe trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.

Answer : C

If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.

Question 6

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?

ASet the weight to a higher than default value.

BSet the weight to a lower than default value.

CSet the priority to a lower than default value.

DSet the priority to a higher than default value.

Answer : A

Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.

Question 7

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?

Aprofiling: enabled: false

Bhyperparameters; optimizer:none

Csearcher: name: single

Dresources: slots_per_trial: 1

Answer : B

To train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO), you need to set the 'optimizer' field to 'none' in the hyperparameters section of the experiment config. This will instruct the ML engine to not use any hyperparameter optimization when training the model.

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