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HP HPE2-N69 Exam Syllabus Topics:TopicDetailsTopic 1Explain how the Machine Learning Development Environment uses resources and schedules workloads Understand the challenges customers face in training DL modelsTopic 2Qualify customers for HPE Machine Learning Development Environment and System Articulate the business case for HPE Machine Learning Development solutionsTopic 3Demonstrate and explain how to use HPE Machine Learning Development Environment Describe the architecture for HPE Machine Learning Development solutionsTopic 4Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment Describe how HPE Machine Learning Development Environment fits in the marketTopic 5Describe the HPE Machine Learning Development Environment software architecture and deployment options Have a conversation with customers about machine learning (ML) and deep learning (DL)Topic 6Size HPE Machine Learning Development Environment and System solutions Understand machine learning (ML) and deep learning (DL) fundamentals

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HP Using HPE Cray AI Development Environment Sample Questions (Q40-Q45):

NEW QUESTION # 40
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?

A. The engineer should access the HPE Machine Learning Development online calculator and input the mode, max_trials, max_length, divisor, and max_runs.B. The engineer should upload the experiment config to the HPE Machine Learning Development Environment WebUl and view the graph of the experiment plan.C. The engineer should run the "det preview-search" command, referencing the experiment config.D. 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: D

NEW QUESTION # 41
What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?

A. Model InferencingB. Experiment trackingC. Premium dedicated supportD. Distributed training

Answer: D

Explanation:
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.

NEW QUESTION # 42
What distinguishes deep learning (DL) from other forms of machine learning (ML)?

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

Answer: B

NEW QUESTION # 43
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 trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.B. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.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: B

NEW QUESTION # 44
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?

A. Set the weight to a lower than default value.B. Set the priority to a higher than default value.C. Set the priority to a lower than default value.D. Set the weight to a higher than default value.

Answer: D

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.

NEW QUESTION # 45
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