HP HPE2-N69 Exam Practice Test Instant Access

Question 1

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 2

What are the mechanics of now a model trains?

ADecides which algorithm can best meet the use case for the application in question

BAdjusts the model's parameter weights such that the model can Better perform its tasks

CTests how accurately the model performs on a wide array of real world data

DDetects Data drift of content drift that might compromise the ML model's performance

Answer : B

This is done by running the model through a training loop, where the model is fed data and the parameter weights are adjusted based on the results of the model's performance on the data. For example, if the model is a neural network, the weights of the connections between the neurons are adjusted based on the results of the model's performance on the data. This process is repeated until the model performs better on the data, at which point the model is considered trained.

Question 3

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 4

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 5

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?

AUsing hyperparameter optimization (HPO)

BDistributing the training across multiple CPUs

CUsing a variable learning late

DTraining the model on multiple epochs

Answer : A

Hyperparameter optimization is a process of tuning the hyperparameters of a machine learning model, such as the number of filters in a convolutional neural network (CNN) model, to determine the best combination of hyperparameters that will result in the best model performance. HPO techniques are used to automatically find the optimal hyperparameter values, which can greatly increase the accuracy and performance of the model.

Question 6

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.

Question 7

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

AHPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

BThey cannot implement HPO on TensorFlow models, so they must move their models to a new framework.

CImplementing HPO manually can be time-consuming and demand a great deal of expertise.

DML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

Answer : C

Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.

HPE2-N69 Using HPE AI and Machine Learning Exam Practice Test