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Undoubtedly, passing the HP HPE2-N69 certification exam is one big achievement. Regardless of how tough the Using HPE Cray AI Development Environment (HPE2-N69) exam is, it serves an important purpose of improving your skills and knowledge of a specific field. Once you become certified by HP, a whole new career scope will open up to you.

HPE2-N69 certification exam is a comprehensive and challenging exam that tests developers on their proficiency in using the HPE Cray AI development environment. HPE2-N69 exam consists of 60 multiple-choice questions and must be completed within 90 minutes. To pass the exam, developers must score at least 70% or higher. Candidates who pass the exam will earn the HPE Cray AI Developer certification, which is recognized globally and highly valued by employers.

HP HPE2-N69 certification is highly valuable for professionals who want to work with the latest AI technologies. It demonstrates to employers that the candidate has the skills and knowledge required to develop and deploy AI solutions using HPE Cray. Using HPE Cray AI Development Environment certification can help professionals advance their careers and increase their earning potential.

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HP Using HPE Cray AI Development Environment Sample Questions (Q24-Q29):

NEW QUESTION # 24
A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?

  • A. Complex and time-consuming hyperparameter optimization (HPO)
  • B. Time-consuming data collection
  • C. Complex model deployment processes
  • D. Complex and time-consuming data cleansing process

Answer: A

Explanation:
The HPE Machine Learning Development Environment specifically addresses Complex and time-consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.


NEW QUESTION # 25
What is one key target vertical (or HPE Machine Learning Development solutions?

  • A. Hospitality
  • B. Retail
  • C. Manufacturing
  • D. K-12education

Answer: C


NEW QUESTION # 26
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 adequate power and cooling for the GPU-enabled servers
  • B. The requirement that the ML team must wait for the IT team to initiate each new training process
  • C. The complexity of adjusting model code to distribute the training process across multiple GPUs
  • D. A lack of understanding of the DL model architecture by the NL engineering team

Answer: C

Explanation:
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.


NEW QUESTION # 27
The 10 agents in "my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting for "resources_per_trial?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: B

Explanation:
The valid setting for "resourcespertrial" for the 10 agents in "my-compute-poor" with 8 GPUs each would be 20, as this would be the total number of GPUs available across all 10 agents. This setting would allow the experiment config to run on multiple GPUs at once.


NEW QUESTION # 28
You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?

  • A. How much time do you spend managing the ML infrastructure?
  • B. What frustrations do you have with existing ML deployment and differencing solutions?
  • C. How much do you understand about building ML and DL models?
  • D. How long does it currently take for a DL training to run the backward pass?

Answer: B

Explanation:
A good discovery question to start a conversation about HPE Machine Learning Development Environment with an IT contact at a customer would be: "What frustrations do you have with existing ML deployment and differencing solutions?" By understanding the customer's current challenges and frustrations, you can better determine how HPE's ML Development Environment could help to address those needs.


NEW QUESTION # 29
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