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Free HPE2-N69 Exam Braindumps (page: 2)

Page 1 of 11
PDF with 40 Questions

You are helping a customer start to implement hyper parameter optimization (HPO) with HPE Machine learning Development Environment. An ML engineer is putting together an experiment config file with the desired Adaptive A5HA settings. The engineer asks you questions, such as how many trials will be trained on the max length and what the min length for all trials will be.
What should you explain?

  1. The engineer should run the "det preview-search" command, referencing the experiment config.
  2. The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.
  3. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.
  4. The engineer should run a preliminary experiment with one tenth the desired number of max trials, assess the results, and then run the full experiment.

Answer(s): B

Explanation:

The engineer should specify the number of trials to train on the max length and the minimum length for all trials in the experiment config file. For example, if the engineer wants to run 10 trials with a max length of 10, the config file should look something like this:
{
"mode": "A5HA",
"max_trials": 10,
"max_length": 10,
"min_length": 1,
"divisor": 2,
"max_runs": 1
}
Once the config file is complete, the engineer should upload it to the HPE Machine Learning Development Environment WebUI and view the graph of the experiment plan. This will allow the engineer to see how the Adaptive A5HA settings will affect the experiment. After that, the engineer can run the experiment and assess the results.



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?

  1. Set the weight to a higher than default value.
  2. Set the weight to a lower than default value.
  3. Set the priority to a lower than default value.
  4. Set the priority to a higher than default value.

Answer(s): A

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.



You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use "det deploy" to set up the cluster.
What is one prerequisite?

  1. installing the NVIDIA Container Toolkit on your local machine
  2. Manually creating the AWS EC2 instance with a PostgreSQL database
  3. Recording the name of a valid AWS EC2 keypair
  4. Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account

Answer(s): C

Explanation:

In order to use the "det deploy" command to set up a cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS), you will need to have a valid AWS EC2 keypair. The keypair will authenticate your access to the cluster and allow you to securely access the cluster once it is set up.



A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS).
What is an advantage of streaming the data during a trial, as opposed to downloading the data?

  1. Streaming requires just one bucket, while downloading requires many.
  2. The trial can more quickly start up and begin training the model.
  3. The trial can better separate training and validation data.
  4. Setting up streaming is easier that setting up downloading.

Answer(s): B

Explanation:

Streaming the data during a trial allows the data to be processed more quickly, as it does not need to be downloaded onto the cluster before training can begin. This means that the trial can start up faster and the model can begin training more quickly.






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