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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: Managing and Operating HPE Private Cloud AI Solutions | 30% | - Explain backup and recovery procedures - Identify troubleshooting procedures and common issues - Describe how to manage users and access control - Describe the tools and methods for managing HPE Private Cloud AI - Explain how to monitor HPE Private Cloud AI performance and health - Identify how to manage storage and data resources |
| Topic 3: Installing and Configuring HPE Private Cloud AI Solutions | 30% | - Identify how to access and use HPE Private Cloud AI management interfaces - Describe how to validate the HPE Private Cloud AI installation - Explain how to deploy and configure HPE Private Cloud AI components - Describe the prerequisites for installing HPE Private Cloud AI - Identify the steps to configure the HPE Private Cloud AI environment |
| Topic 4: 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 - Explain the HPE Private Cloud AI sizing and configuration guidelines - Explain common AI use cases and how they map to workloads - Describe the AI/ML lifecycle and data pipeline requirements |
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NEW QUESTION # 27
An architect is selecting the appropriate HPE Private Cloud AI configuration for a customer. The customer's primary workload is fine-tuning a 70B parameter model, a task that is extremely sensitive to compute latency and requires the highest possible performance for multi-node distributed training.
Which specific infrastructure components included in the "Large" configuration of HPE Private Cloud AI are essential for meeting this high-performance training requirement? (Select all that apply.)
Answer: C,D,E
NEW QUESTION # 28
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: C
NEW QUESTION # 29
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: D
NEW QUESTION # 30
A hospital is developing an AI application to automatically detect specific anomalies in medical images like X-rays and MRIs. The task requires the model to learn and identify complex spatial patterns, such as the shapes and textures of tissues and potential tumors.
Which type of neural network architecture is specifically designed for and best suited to this kind of image analysis task? (Select all that apply.)
Answer: A,C,E
NEW QUESTION # 31
What is the primary purpose of using RDMA over Converged Ethernet (RoCE) in the AI interconnect fabric of an HPE Private Cloud AI solution?
Answer: B
NEW QUESTION # 32
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