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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.
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The questions for HPE2-N69 were last updated on Jan 4, 2025.
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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?
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.
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?
Adaptive ASHA is an enhanced version of ASHA that uses a reinforcement learning approach to select hyperparameter configurations. This allows Adaptive ASHA to select higher-performing configs and clone those configurations, allowing for better performance than ASHA.
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?
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.
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.
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.
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