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HPE Private Cloud AI Solutions Sample Questions:

1. An AI development team is training a large language model that exceeds the memory capacity of a single GPU. To continue training, they need to distribute the workload across multiple GPUs within the same server.
Which combination of HPE server and NVIDIA technology is specifically designed to provide a high- bandwidth, direct communication path between GPUs, bypassing the PCIe bus for improved performance in multi-GPU training?

A) HPE ProLiant DL380a Gen11 server with GPUDirect Storage (GDS)
B) HPE ProLiant DL380a Gen12 server with an NVIDIA NVLink Bridge
C) HPE Alletra Storage MP with RoCE-capable network adapters
D) HPE ProLiant DL325 Gen11 server with NVIDIA L4 GPUs

2. What is the primary advantage of using a Smart Template in OCA for HPE Private Cloud AI versus manually configuring a similar set of hardware?

A) The Smart Template guarantees the lowest possible price for the hardware.
B) The Smart Template provides a 90-day free trial of the entire solution.
C) The Smart Template allows the use of components from other vendors.
D) The Smart Template ensures all necessary and validated components, including specific cables, power cords, and software SKUs, are included correctly.

3. An enterprise architecture team is debating the best method to adapt a general-purpose Large Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly changing knowledge base of product manuals and technical notes. Verifiability and traceability of the information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)

A) Use Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
B) Use both RAG and fine-tuning for both use cases as they are always used together.
C) Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the model's behavior.
D) Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.
E) Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.

4. A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?

A) The Large configuration does not support Retrieval-Augmented Generation (RAG).
B) The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
C) The Medium configuration is the only one that includes the HPE AI Essentials software.
D) The Medium configuration has more storage capacity than the Large configuration.

5. 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?

A) A model development and experimentation workload.
B) A classic AI inferencing workload.
C) A RAG (Retrieval-Augmented Generation) inferencing workload.
D) A large-scale model training workload.

Solutions: