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Most Recent HPE2-N69 Exam Dumps
Prepare for the HP Using HPE AI and Machine Learning exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.
QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the HPE2-N69 exam and achieve success.
The questions for HPE2-N69 were last updated on May 2, 2025.
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You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion. With the customer's dedicated IT staff, the customer is ready to deploy an on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment is a comprehensive solution that provides the tools and technologies required to develop, manage, and deploy ML models. It includes a distributed training framework, an orchestration layer, a powerful development environment, and an integrated MLOps platform. With this solution, the customer can expand their ML/DL projects and scale up their team.
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 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.
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?
If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.
An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO). What experiment config fields configure this behavior?
To train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO), you need to set the 'optimizer' field to 'none' in the hyperparameters section of the experiment config. This will instruct the ML engine to not use any hyperparameter optimization when training the model.
You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?
The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.
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