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HPE Cray AI Development Environment is a comprehensive platform that provides users with the tools and resources they need to build and deploy AI applications. It allows users to develop, train, and deploy their AI models with ease, and it comes equipped with a range of powerful features and tools, including data preparation, model training, and deployment.
HPE Cray AI development environment is a cutting-edge platform that enables developers to build and deploy AI models quickly and efficiently. This platform provides developers with a range of tools and resources that make it easy to create and optimize AI models, including access to high-performance computing resources and machine learning libraries. With this certification, developers can prove that they have the skills and knowledge to work with this powerful platform and harness its full potential.
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HP Using HPE Cray AI Development Environment Sample Questions (Q15-Q20):
NEW QUESTION # 15
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 priority to a higher than default value.
- B. Set the weight to a higher than default value.
- C. Set the priority to a lower than default value.
- D. Set the weight to a lower than default value.
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 # 16
What are the mechanics of now a model trains?
- A. Tests how accurately the model performs on a wide array of real world data
- B. Detects Data drift of content drift that might compromise the ML model’s performance
- C. Adjusts the model’s parameter weights such that the model can Better perform its tasks
- D. Decides which algorithm can best meet the use case for the application in question
NEW QUESTION # 17
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
- A. Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
- B. InfiniBand
- C. Data Center Bridging (OCB)-enabled Ethernet
- D. Slingshot
NEW QUESTION # 18
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. It is primarily used to transport trained models to a deployment environment.
- B. it provides pipelines to manage the complete lifecycle.
- C. it helps engineers use a language like Python to code and trail DL models.
- D. It adds system and GPU monitoring to the training process.
NEW QUESTION # 19
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
- A. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
- B. Trial I is allowed to finish. Then Trial 3 is scheduled.
- C. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- D. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
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.
NEW QUESTION # 20
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