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NEW QUESTION 49
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to spend less time on creating the code tor models and more time training models.
- B. The team wants to avoid training models to the point where they perform less well on new data.
- 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: B
Explanation:
Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.
NEW QUESTION 50
A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?
- A. Set the priority to a higher than default value.
- B. Set the weight to a higher than default value.
- C. Set the priority to a lower than default value.
- D. Set the weight to a lower than default value.
Answer: B
Explanation:
Fair-share scheduling allocates resources to experiments based on the weight value of the resource pool. Increasing the weight value of a resource pool will result in more resource slots being allocated to it.
NEW QUESTION 51
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that the highest priority experiments obtain access to more resources
- B. Ensuring that all experiments receive their fair share of resources
- C. Equally distributing utilization across multiple agents
- D. Minimizing costs in a cloud environment
Answer: D
NEW QUESTION 52
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. How long does it currently take for a DL training to run the backward pass?
- B. How much do you understand about building ML and DL models?
- C. What frustrations do you have with existing ML deployment and differencing solutions?
- D. How much time do you spend managing the ML infrastructure?
Answer: C
Explanation:
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.
NEW QUESTION 53
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