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HPE2-N69 Exam Questions

Status: RETIRED
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: 30-07-2024)
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

Burma

2 months ago
Passing the HP Using HPE AI and Machine Learning exam was a great achievement for me, and I attribute my success to using Pass4Success practice questions. The exam covered important topics such as understanding the challenges customers face in training DL models. One question that I recall from the exam was about the specific challenges in training deep learning models and how the development environment addresses them. Despite some uncertainty, I was able to pass the exam successfully.
upvoted 0 times
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Cassi

2 months ago
Passed the exam with flying colors! Deep learning architectures were a big focus - prepare to identify suitable models for given use cases. Review CNNs, RNNs, and transformers. Pass4Success practice tests were invaluable for last-minute prep!
upvoted 0 times
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Jerry

3 months ago
My exam experience was successful as I passed the HP Using HPE AI and Machine Learning exam with the assistance of Pass4Success practice questions. The exam included topics on how the Machine Learning Development Environment uses resources and schedules workloads. One question that I remember from the exam was about the resources allocation process in the development environment. Although I had some doubts about the answer, I was able to pass the exam.
upvoted 0 times
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Eva

3 months ago
Grateful to Pass4Success for helping me pass the HPE AI and ML exam. Their practice tests were a game-changer!
upvoted 0 times
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Hassie

3 months ago
The exam covered a lot on HPE's AI solutions. Be ready for scenario-based questions about choosing the right HPE hardware for specific AI workloads. Brush up on HPE Apollo and Edgeline systems. Pass4Success really helped me prepare quickly!
upvoted 0 times
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Giovanna

4 months ago
I recently passed the HP Using HPE AI and Machine Learning exam with the help of Pass4Success practice questions. The exam covered topics such as how the Machine Learning Development Environment helps customers surmount their challenges and understanding the challenges customers face in training DL models. One question that stood out to me was related to running a proof of concept (PoC) using the development environment. Despite being unsure of the answer, I managed to pass the exam.
upvoted 0 times
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Elfrieda

4 months ago
Just passed the HP Certified: Using HPE AI and ML exam! One key area was MLOps - expect questions on deployment strategies and model monitoring. Study the MLOps lifecycle thoroughly. Thanks Pass4Success for the spot-on practice questions!
upvoted 0 times
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Dana

4 months ago
Aced the HP Certified exam thanks to Pass4Success. Their questions were incredibly similar to the real thing. Highly recommend!
upvoted 0 times
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Melodie

4 months ago
Passed the HP Certified exam with flying colors! Pass4Success's materials were key to my efficient preparation. Thank you!
upvoted 0 times
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Lynelle

4 months ago
Just passed the HPE AI and ML exam! Thanks Pass4Success for the spot-on practice questions. Saved me weeks of prep time!
upvoted 0 times
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Yuette

5 months ago
Pass4Success came through big time! Their HPE AI exam materials were crucial for my quick preparation and success.
upvoted 0 times
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Free HP HPE2-N69 Exam Actual Questions

Note: Premium Questions for HPE2-N69 were last updated On 30-07-2024 (see below)

Question #1

You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster. What software must be installed on the machine before you run that command?

Reveal Solution Hide Solution
Correct Answer: D

Before running the 'del deploy' command to set up the cluster, you must first install Docker on the machine. Docker is a containerization platform that is used to run applications in an isolated environment. It is necessary to have Docker installed before running the 'del deploy' command to set up the cluster for the open source Determined AI on a local machine.


Question #2

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?

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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 #3

You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use "del deploy" to set up the cluster. What software must be installed on the machine before you run that command?

Reveal Solution Hide Solution
Correct Answer: D

Before running the 'del deploy' command to set up the cluster, you must first install Docker on the machine. Docker is a containerization platform that is used to run applications in an isolated environment. It is necessary to have Docker installed before running the 'del deploy' command to set up the cluster for the open source Determined AI on a local machine.


Question #4

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

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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

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

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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.



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