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NEW QUESTION 34
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?

A. They run training workloads that do not require GPUs.B. They run validation and checkpoint workloads.C. They host management software such as the conductor and HPCM.D. They run non-distributed training workloads.

Answer: C

 

NEW QUESTION 35
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?

A. hyperparameters; optimizer:noneB. resources: slots_per_trial: 1C. searcher: name: singleD. profiling: enabled: false

Answer: B

 

NEW QUESTION 36
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

A. Implementing HPO manually can be time-consuming and demand a great deal of expertise.B. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.C. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.D. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

Answer: C

 

NEW QUESTION 37
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?

A. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.B. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.C. The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.D. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.

Answer: D

 

NEW QUESTION 38
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

A. Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.B. ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.C. Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.D. Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.

Answer: A

 

NEW QUESTION 39
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