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HPE2-N69 - Using HPE AI and Machine Learning

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HPE2-N69 Questions and Answers

Question # 1

What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?


Ensuring that all experiments receive their fair share of resources


Minimizing costs in a cloud environment


Equally distributing utilization across multiple agents


Ensuring that the highest priority experiments obtain access to more resources

Question # 2

You are helping a customer start to implement hyper parameter optimization (HPO) with HPE Machine learning Development Environment. An ML engineer is putting together an experiment config file with the desired Adaptive A5HA settings. The engineer asks you questions, such as how many trials will be trained on the max length and what the min length for all trials will be.

What should you explain?


The engineer should run the "det preview-search" command, referencing the experiment config.


The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.


The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.


The engineer should run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.

Question # 3

What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?


They run validation and checkpoint workloads.


They run training workloads that do not require GPUs.


They host management software such as the conductor and HPCM.


They run non-distributed training workloads.

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