Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

HPE2-N69 Exam

Exam Name: Using HPE AI and Machine Learning
Exam Code: HPE2-N69
Related Certification(s):
  • HPE Product Certified Certifications
  • HP AI and Machine Learning [2022] Certifications
Certification Provider: HP
Actual Exam Duration: 40 Minutes
Number of HPE2-N69 practice questions in our database: 40 (updated: Jun. 07, 2024)
Expected HPE2-N69 Exam Topics, as suggested by HP :
  • Topic 1: Explain how HPE Machine Learning Development Environment helps customers surmount their challenges/ Run a proof of concept (PoC)
  • Topic 2: Explain how the Machine Learning Development Environment uses resources and schedules workloads/ Understand the challenges customers face in training DL models
  • Topic 3: Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment/ Describe how HPE Machine Learning Development Environment fits in the market
  • Topic 4: Describe the HPE Machine Learning Development Environment software architecture and deployment options/ Have a conversation with customers about machine learning (ML) and deep learning (DL)
  • Topic 5: Size HPE Machine Learning Development Environment and System solutions/ Understand machine learning (ML) and deep learning (DL) fundamentals
  • Topic 6: Qualify customers for HPE Machine Learning Development Environment and System/ Articulate the business case for HPE Machine Learning Development solutions
  • Topic 7: Demonstrate and explain how to use HPE Machine Learning Development Environment/ Describe the architecture for HPE Machine Learning Development solutions
Disscuss HP HPE2-N69 Topics, Questions or Ask Anything Related

Currently there are no comments in this discussion, be the first to comment!

Free HP HPE2-N69 Exam Actual Questions

Note: Premium Questions for HPE2-N69 were last updated On Jun. 07, 2024 (see below)

Question #1

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

Reveal Solution Hide Solution
Correct 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 #2

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

Reveal Solution Hide Solution
Correct 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 #3

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?

Reveal Solution Hide Solution
Correct 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 #4

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

Reveal Solution Hide Solution
Correct 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 #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?

Reveal Solution Hide Solution
Correct 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.



Unlock Premium HPE2-N69 Exam Questions with Advanced Practice Test Features:
  • Select Question Types you want
  • Set your Desired Pass Percentage
  • Allocate Time (Hours : Minutes)
  • Create Multiple Practice tests with Limited Questions
  • Customer Support
Get Full Access Now

Save Cancel