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HP Using HPE Cray AI Development Environment Sample Questions (Q22-Q27):

NEW QUESTION # 22
What distinguishes deep learning (DL) from other forms of machine learning (ML)?

  • A. Models based on neural networks with interconnected layers of nodes, including multiple hidden layers
  • B. Models that are trained through unsupervised, rather than supervised, training
  • C. Models defined with Apache Spark rather than MapReduce
  • D. Models trained through multiple training processes implemented by different team members

Answer: A

Explanation:
Models based on neural networks with interconnected layers of nodes, including multiple hidden layers. Deep learning (DL) is a type of machine learning (ML) that uses models based on neural networks with interconnected layers of nodes, including multiple hidden layers. This is what distinguishes it from other forms of ML, which typically use simpler models with fewer layers. The multiple layers of DL models enable them to learn complex patterns and features from the data, allowing for more accurate and powerful predictions.


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

Answer: A


NEW QUESTION # 24
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

  • A. Establishing multiple compute resource pools on the cluster, one tor servers or each type
  • B. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
  • C. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
  • D. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs

Answer: A

Explanation:
By establishing multiple compute resource pools on the cluster, you can ensure that the correct servers are used for each experiment, depending on the number of GPUs required. This will help ensure that the experiments are run on the servers with the correct resources without having to manually assign each experiment to the appropriate server.


NEW QUESTION # 25
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 the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
  • B. Make sure that you have already logged into the cluster with the "det login'' command.
  • C. Re-enter the command with "-m" in which is the code filename.
  • D. Re-enter the command with a period (.) at the end.

Answer: A

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 # 26
What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

  • A. it downloads datasets for training.
  • B. It ensures experiment metadata is stored.
  • C. It uploads model checkpoints.
  • D. It validates trained models.

Answer: B

Explanation:
The conductor of an HPE Machine Learning Development Environment cluster is responsible for ensuring that all experiment metadata is stored and accessible. This includes tracking experiment runs, storing configuration parameters, and ensuring results are stored for future reference.


NEW QUESTION # 27
......

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