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NEW QUESTION 23
You are in a directory on your machine with your experiment config file and your model code. You enter this command:
det experiment create myfile.yaml
You receive this error:
det experiment create: error: the following arguments are required: model_def What should you do?

A. Make sure that you have already logged into the cluster with the "det login'' command.B. Re-enter the command with a period (.) at the end.C. Re-enter the command with "-m" in which is the code filename.D. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.

Answer: D

Explanation:
Make sure that the myfile.yaml tile includes code for a PyTorchTrial or TFKerasTrial class. When creating an experiment with the det experiment create command, you need to specify the model_def parameter to provide the code for the PyTorchTrial or TFKerasTrial class. This code should be specified in the myfile.yaml file, so make sure that the myfile.yaml file includes the code for the model you want to use.

 

NEW QUESTION 24
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. Training the model on multiple epochsB. Using hyperparameter optimization (HPO)C. Distributing the training across multiple CPUsD. Using a variable learning late

Answer: A

 

NEW QUESTION 25
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 conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.B. The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.C. The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.D. The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.

Answer: B

Explanation:
The best way to explain how HPE Machine Learning Development Environment addresses this pain point is to mention that the solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint. This ensures that in case of a hardware failure, the engineers will not lose their work and training can be resumed from the last successful checkpoint.

 

NEW QUESTION 26
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. What frustrations do you have with existing ML deployment and differencing solutions?B. How long does it currently take for a DL training to run the backward pass?C. How much do you understand about building ML and DL models?D. How much time do you spend managing the ML infrastructure?

Answer: B

 

NEW QUESTION 27
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