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Download Using HPE Cray AI Development Environment Exam Dumps

NEW QUESTION 49
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

A. Minimizing costs in a cloud environmentB. Equally distributing utilization across multiple agentsC. Ensuring that the highest priority experiments obtain access to more resourcesD. Ensuring that all experiments receive their fair share of resources

Answer: A

 

NEW QUESTION 50
What is the role of a hidden layer in an artificial neural network (ANN)?

A. It is responsible for passively reformatting data for use in the ANN.B. It receives and weighs inputs from the preceding layer and produces outputs for the next layer.C. It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.D. It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.

Answer: C

 

NEW QUESTION 51
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 conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.B. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.

Answer: A

 

NEW QUESTION 52
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

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

Answer: B

 

NEW QUESTION 53
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. Re-enter the command with a period (.) at the end.B. Make sure that you have already logged into the cluster with the "det login'' command.C. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.D. Re-enter the command with "-m" in which is the code filename.

Answer: C

 

NEW QUESTION 54
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