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NEW QUESTION 43
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 not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
- B. The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
- 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: D
Explanation:
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion. With the customer’s dedicated IT staff, the customer is ready to deploy an on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment is a comprehensive solution that provides the tools and technologies required to develop, manage, and deploy ML models. It includes a distributed training framework, an orchestration layer, a powerful development environment, and an integrated MLOps platform. With this solution, the customer can expand their ML/DL projects and scale up their team.
NEW QUESTION 44
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment.
That GPU fails. What happens next?
- A. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- B. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- C. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
- D. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
Answer: D
NEW QUESTION 45
ML engineers are defining a convolutional neural network (CNN) model bur they are not sure how many filters to use in each convolutional layer. What can help them address this concern?
- A. Using a variable learning late
- B. Distributing the training across multiple CPUs
- C. Training the model on multiple epochs
- D. Using hyperparameter optimization (HPO)
Answer: D
Explanation:
Hyperparameter optimization is a process of tuning the hyperparameters of a machine learning model, such as the number of filters in a convolutional neural network (CNN) model, to determine the best combination of hyperparameters that will result in the best model performance. HPO techniques are used to automatically find the optimal hyperparameter values, which can greatly increase the accuracy and performance of the model.
NEW QUESTION 46
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