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| Section | Weight | Objectives |
|---|---|---|
| Managing and Operating HPE Private Cloud AI Solutions | 30% | - Describe the tools and methods for managing HPE Private Cloud AI - Explain backup and recovery procedures - 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 - Identify how to manage storage and data resources |
| Supporting HPE Private Cloud AI Solutions | 20% | - Describe support resources and documentation - Describe capacity planning and optimization best practices - Identify how to perform firmware and software updates - Explain how to work with HPE support services |
| Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Identify how to access and use HPE Private Cloud AI management interfaces - Identify the steps to configure the HPE Private Cloud AI environment - 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 |
| 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 - Describe the AI/ML lifecycle and data pipeline requirements - Identify components of the HPE Private Cloud AI architecture - Explain the HPE Private Cloud AI sizing and configuration guidelines |
>> Sample HPE2-B08 Questions <<
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NEW QUESTION # 17
A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?
Answer: C
NEW QUESTION # 18
A company has developed a custom fraud detection model. Their data science team wants to deploy this model into production for other applications to use. They want to avoid the complexity of manually configuring the serving environment, optimizing for hardware, and creating a scalable API endpoint.
How does using an NVIDIA Inference Microservice (NIM) within HPE Private Cloud AI simplify this process?
Answer: C
NEW QUESTION # 19
A customer's ML Engineer states, "We need to deploy our trained models, and our top priority is simplifying the process. We want to treat our models like cattle, not pets-packaging them into standardized, optimized containers that we can deploy and scale easily via an API." This statement describes the primary benefit of which software component in the HPE Private Cloud AI stack?
Answer: C
NEW QUESTION # 20
An architect is meeting with a prospective customer to determine the right HPE AI solution. The customer provides the following information about their situation.
```
- AI Status: No formal AI strategy. One successful PoC for theft prevention using computer vision is running at a single store.
- Goal: Wants to explore creating a "digital twin" of their supply chain for simulation, but has no clear KPIs.
- Team: Two data scientists, one ML engineer.
- Infrastructure: Ad-hoc use of public cloud for the PoC; no standardized tech stack.
```
Based on this profile, which AI maturity level and corresponding HPE solution should the architect initially position? (Choose 2.)
Answer: A,C
NEW QUESTION # 21
An IT director for a regional retail chain tells you they are "doing AI." Upon further questioning, you learn they have one data scientist who has built a single, experimental sales forecasting model as a proof-of-concept (PoC). The project lacks clear KPIs for success and there is no formal strategy for how to productionize it or what to do next.
How would you classify this customer's AI maturity level?
Answer: D
NEW QUESTION # 22
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