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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Supporting HPE Private Cloud AI Solutions | 20% | - Describe support resources and documentation - Explain how to work with HPE support services - Identify how to perform firmware and software updates - Describe capacity planning and optimization best practices |
| Topic 2: Architecting HPE Private Cloud AI Solutions | 20% | - Identify components of the HPE Private Cloud AI architecture - Describe how HPE Private Cloud AI supports AI/ML workloads - Describe the AI/ML lifecycle and data pipeline requirements - Explain common AI use cases and how they map to workloads - Explain the HPE Private Cloud AI sizing and configuration guidelines |
| Topic 3: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Describe the prerequisites for installing HPE Private Cloud AI - Explain how to deploy and configure HPE Private Cloud AI components - Describe how to validate the HPE Private Cloud AI installation - Identify the steps to configure the HPE Private Cloud AI environment - Identify how to access and use HPE Private Cloud AI management interfaces |
| Topic 4: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Identify how to manage storage and data resources - Identify troubleshooting procedures and common issues - Describe how to manage users and access control - Explain backup and recovery procedures - Describe the tools and methods for managing HPE Private Cloud AI - Explain how to monitor HPE Private Cloud AI performance and health |
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NEW QUESTION # 13
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: C
NEW QUESTION # 14
A customer is expanding their HPE Private Cloud AI "Medium" configuration to support a new generative AI inferencing workload. They are concerned about network congestion and latency, as the AI workload is known to generate large, sudden bursts of traffic between the compute nodes and the storage system.
The solution uses NVIDIA Spectrum SN4700M switches for the AI interconnect.
Which feature of these switches is specifically designed to handle bursty traffic and prevent packet loss in a lossless Ethernet fabric?
Answer: C
NEW QUESTION # 15
You are in a meeting with the VP of Manufacturing for a large industrial company. She describes their AI program as follows:
"We have a formal Center of Excellence for AI that reports directly to the CTO. Our strategy is to scale our successful predictive maintenance models across all 25 of our global plants. We have standardized on a consistent cloud and data governance strategy for all AI projects and have clear ROI targets for reducing downtime." Based on this description, which AI maturity level best describes this customer?
Answer: D
NEW QUESTION # 16
A data science team is struggling to manage their AI/ML projects. They use a variety of open-source tools for data preparation, training, and MLOps, but integrating them is complex and time-consuming.
They need a unified platform that provides self-service access to a curated and pre-integrated set of these tools.
Which HPE Private Cloud AI software component is specifically designed to solve this problem?
Answer: B
NEW QUESTION # 17
In a Natural Language Processing (NLP) task, what is the role of a tokenizer?
Answer: D
NEW QUESTION # 18
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