HPE2-N69 Using HPE AI and Machine Learning Exam Practice Test

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Total 40 questions
Question 1

What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?



Answer : C

The benefit of HPE Machine Learning Development Environment beyond open source Determined AI is Distributed Training. Distributed training allows multiple machines to train a single model in parallel, greatly increasing the speed and efficiency of the training process. HPE ML Development Environment provides tools and support for distributed training, allowing users to make the most of their resources and quickly train their models.


Question 2

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?



Answer : A

HPE Machine Learning Development System uses Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE) for high-speed, agent-to-agent communications. This technology allows data to be transferred directly between agents without the need for copying, which results in improved performance and reduced latency.


Question 3

An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:

* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50

* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I

What happens?



Answer : D

Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.


Question 4

You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?



Answer : D

The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.


Question 5

You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?



Answer : D

The license count for the HPE Machine Learning Development Environment solution would be based on the number of processor cores on all servers in the cluster. This includes all servers in the cluster, regardless of whether they are running agents or not. Each processor core in the cluster requires a license and these licenses can be purchased in packs of 2, 4, 8, and 16.


Question 6

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?



Answer : B

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.


Question 7

A customer mentions that the ML team wants to avoid overfitting models. What does this mean?



Answer : C

Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.


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Total 40 questions