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NEW QUESTION 34
An ML engineer is running experiments on HPE Machine Learning Development Environment. The engineer notices all of the checkpoints for a trial except one disappear after the trial ends. The engineer wants to Keep more of these checkpoints. What can you recommend?

A. Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.B. Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.C. Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.D. Double-checking that the checkpoint storage location is operating under 90% of total capacity.

Answer: C

Explanation:
The best recommendation for an ML engineer running experiments on HPE Machine Learning Development Environment to keep more of the checkpoints is to adjust the experiment config's checkpoint storage settings to save more of the latest and best checkpoints. This can be done by monitoring ongoing trials in the WebUI and clicking checkpoint flags to auto-save the desired checkpoints. Additionally, the engineer should double-check that the checkpoint storage location is operating under 90% of total capacity to ensure that enough capacity is available to store the checkpoints. Finally, they can adjust the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage if desired.

 

NEW QUESTION 35
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

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

Answer: C

 

NEW QUESTION 36
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?

A. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.B. The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.C. The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.D. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.

Answer: C

 

NEW QUESTION 37
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?

A. The team wants to avoid training models to the point where they perform less well on new data.B. The team wants to spend less time on creating the code tor models and more time training models.C. The team wants to spend less time figuring out which CPUs are available for training models.D. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.

Answer: C

 

NEW QUESTION 38
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?

A. The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.B. 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.C. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.D. The engineer should run the "det preview-search" command, referencing the experiment config.

Answer: B

 

NEW QUESTION 39
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