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| Section | Weight | Objectives |
|---|---|---|
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe how to validate the HPE Private Cloud AI installation - Describe the prerequisites for installing HPE Private Cloud AI - Explain how to deploy and configure HPE Private Cloud AI components - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces |
| Architecting HPE Private Cloud AI Solutions | 20% | - Explain common AI use cases and how they map to workloads - Describe how HPE Private Cloud AI supports AI/ML workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture |
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify how to manage storage and data resources - Describe the tools and methods for managing HPE Private Cloud AI - Explain how to monitor HPE Private Cloud AI performance and health - Describe how to manage users and access control - Identify troubleshooting procedures and common issues - Explain backup and recovery procedures |
| Supporting HPE Private Cloud AI Solutions | 20% | - Explain how to work with HPE support services - Describe capacity planning and optimization best practices - Identify how to perform firmware and software updates - Describe support resources and documentation |
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NEW QUESTION # 49
An enterprise wants to deploy pre-trained foundation models from various sources for multiple business units. A key requirement is to simplify and standardize the deployment process, regardless of the model's origin. They want a solution that packages models into scalable, optimized, and easy-to-use microservices with a standard API.
Which component of the NVIDIA AI Enterprise software suite directly addresses this need?
Answer: A
NEW QUESTION # 50
A customer needs a solution for two primary workloads: large-scale model training and real-time inference. They have a team of data scientists who are constantly developing new models and a separate operations team that deploys and manages these models in production.
Which statement best describes how the different stakeholders would interact with the HPE Private Cloud AI solution?
Answer: A
NEW QUESTION # 51
A customer explains that their data engineers are spending too much time managing disparate data pipelines with a complex set of open-source tools. They are an 'Early AI User' trying to standardize their approach.
Which value proposition of HPE Private Cloud AI directly addresses this specific stakeholder's pain point?
Answer: B
NEW QUESTION # 52
A customer states they need an AI solution for a "summarization" use case that will be accessed by approximately 75 concurrent users. The underlying data is updated frequently, so RAG will be required.
When using the HPE Intelligent Configurator, which three inputs are mandatory to get an initial sizing recommendation?
Answer: C
NEW QUESTION # 53
An organization is deploying a multi-tenant AI environment using HPE Private Cloud AI. They need to run several smaller, independent AI inference workloads on a single, powerful NVIDIA H100 GPU to maximize resource utilization. Each workload must be securely isolated with its own dedicated portion of the GPU's compute and memory resources.
What key NVIDIA technology, supported on the Hopper architecture, allows for this secure partitioning of a single physical GPU? (Select all that apply.)
Answer: B,E
NEW QUESTION # 54
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