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HPE Private Cloud AI Solutions Sample Questions:

1. A large enterprise is adopting HPE Private Cloud AI and needs to accelerate the development of several generative AI applications. Their data science team wants to leverage pre-trained foundation models from NVIDIA but needs to customize them for specific business tasks like contract summarization and internal policy Q&A.
Which NVIDIA AI Enterprise software framework provides a comprehensive, end-to-end toolkit for curating data, customizing models using techniques like PEFT, and implementing guardrails for safe deployment?
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Customer Goal:
- Accelerate development of custom generative AI apps
- Utilize pre-trained foundation models
- Require tools for data prep, model customization, and safety
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A) NVIDIA Triton Inference Server
B) NVIDIA RAPIDS
C) NVIDIA NIM (NVIDIA Inference Microservices)
D) NVIDIA NeMo

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

3. A development team reports that their custom-trained Large Language Model (LLM) is "hallucinating"
- generating factually incorrect or nonsensical information, especially when asked questions outside the scope of its training data. The model was created by fine-tuning a foundation model on a large but static internal dataset. The team wants to improve the model's factual accuracy and reliability without embarking on a new, large-scale training project.
Which are the most appropriate strategies to mitigate this issue? (Choose 2.)

A) Implement a Retrieval-Augmented Generation (RAG) framework to provide the model with verifiable, external context at inference time.
B) Increase the number of hidden layers in the model's architecture.
C) Apply stricter content moderation and safety guardrails to the model's output.
D) Reduce the "temperature" setting during inference to make the model's output less random and more focused.
E) Retrain the model from scratch using a much larger and more diverse public dataset.

4. After using the HPE Intelligent Configurator, an architect reviews the output for a proposed HPE Private Cloud AI solution.
In addition to the BOM, what other critical information does the tool provide that is essential for site readiness planning? (Select all that apply.)

A) A sample Python script for running the first inference job.
B) A list of recommended HPE education courses for the IT staff.
C) A list of recommended AI models for the specified use case.
D) The total weight of the solution for data center floor loading considerations.
E) The estimated power consumption in kW per rack.

5. 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?

A) A fully shared buffer architecture that can dynamically absorb traffic bursts from any port.
B) Integrated Silicon Root of Trust to validate the switch firmware integrity.
C) Support for Fibre Channel over Ethernet (FCoE) encapsulation.
D) The ability to route traffic based on application-layer metadata.

Solutions: