Using HPE AI and Machine Learning HPE2-N69 Dumps Questions

Using HPE AI and Machine Learning HPE2-N69 dumps questions have been cracked, which are the latest version in the whole market. This HPE2-N69 exam tests the candidate's ability to display competency in the use of the HPE Machine Learning Development environment, including the ability to understand the challenges customers face in training Deep Learning models, describe how HPE Machine Learning Development Environment fits in the market, and design and use HPE Machine Learning Development Environment and System solutions. To prepare HP certification HPE2-N69 exam, the official HPE2-N69 exam information is helpful in the preparation. 

HP Certification HPE2-N69 exam basic information is available below. 

Exam type: Web based

Exam duration: 1 hour 30 minutes

Exam length: 40 questions

Passing score: 65%

Delivery languages: English

Using HPE AI and Machine Learning HPE2-N69 exam objectives cover the following details. 

Understand machine learning (ML) and deep learning (DL) fundamentals    24%

Articulate the business case for HPE Machine Learning Development solutions  13%

Describe the architecture for HPE Machine Learning Development solutions     15%

Demonstrate and explain how to use HPE Machine Learning Development Environment     33%

Engage with customers    15%

All the latest HP Certification HPE2-N69 exam dumps questions are the best material for you to test all the above Using HPE AI and Machine Learning HPE2-N69 objectives. Share some HP Certification HPE2-N69 exam dumps questions and answer below. 

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

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

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

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

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

Answer: D

2. What are the mechanics of now a model trains?

A.Decides which algorithm can best meet the use case for the application in question

B.Adjusts the model's parameter weights such that the model can Better perform its tasks

C.Tests how accurately the model performs on a wide array of real world data

D.Detects Data drift of content drift that might compromise the ML model's performance

Answer: A

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

A.Models based on neural networks with interconnected layers of nodes, including multiple hidden layers

B.Models defined with Apache Spark rather than MapReduce

C.Models that are trained through unsupervised, rather than supervised, training

D.Models trained through multiple training processes implemented by different team members

Answer: C

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?

A.A lack of understanding of the DL model architecture by the NL engineering team

B.The complexity of adjusting model code to distribute the training process across multiple GPUs

C.A lack of adequate power and cooling for the GPU-enabled servers

D.The requirement that the ML team must wait for the IT team to initiate each new training process

Answer: A

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?

A.Using hyperparameter optimization (HPO)

B.Distributing the training across multiple CPUs

C.Using a variable learning late

D.Training the model on multiple epochs

Answer: D