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HPE Private Cloud AI Solutions Sample Questions:

1. The three pre-defined sizes (Small, Medium, Large) of HPE Private Cloud AI are designed to address different primary workloads. Match the configuration size to its intended primary workload.
*Configuration Size:
1. Small
2. Medium
3. Large
*Primary Workload:
a. AI Inferencing with RAG and large-scale Fine-Tuning
b. AI Inferencing
c. AI Inferencing with RAG

A) 1-a, 2-b, 3-c
B) 1-c, 2-a, 3-b
C) 1-b, 2-a, 3-c
D) 1-b, 2-c, 3-a

2. What is the primary architectural advantage of the NVIDIA Grace Hopper Superchip (e.g., GH200) for large-scale AI workloads?

A) It uses on-chip encryption to create a confidential computing environment for the CPU.
B) It combines a CPU and a GPU on a single superchip, connected by a high-speed, low-latency NVLink-C2C interconnect.
C) It replaces the need for server memory (DRAM) by using the GPU's global memory exclusively.
D) It is the first NVIDIA GPU to feature fourth-generation Tensor Cores for enhanced matrix calculations.

3. After an architect selects the "HPE Private Cloud AI - Large - Expanded" Smart Template in OCA, they see it includes a significant number of "HPE Factory Express Complex Unit of SVC" services.
What is the purpose of these bundled services?

A) To provide on-site training for the customer's data science team.
B) To provide a credit for future software purchases on the HPE GreenLake cloud.
C) To allow the customer to exchange the hardware for the next generation at no cost.
D) To cover the pre-integration and configuration of the entire solution at an HPE factory before shipment.

4. An architect is designing an AI solution to create a medical chatbot that assists doctors by answering questions based on the latest published medical research. The system must be highly reliable, and its answers must be traceable to the source publications. The customer has highlighted that their internal data science team lacks the expertise for complex model retraining but can manage data ingestion pipelines.
Given the customer requirements, which architectural components should the architect include in the solution design? (Select all that apply.)
```
Customer Requirements:
- AI Use Case: Medical Research Q&A Chatbot
- Key Constraint: Information must be current and verifiable.
- Team Skills: Limited AI model training expertise.
- Data Source: Continuously updated database of medical journals.
```

A) A data ingestion and embedding pipeline to process and add new research to the vector database.
B) A pre-trained, general-purpose Large Language Model (LLM).
C) A vector database to store embeddings of the medical research papers.
D) A Retrieval-Augmented Generation (RAG) framework to query the vector database.
E) An edge-optimized server for model deployment in individual hospital rooms.
F) A process for daily fine-tuning of the base LLM on all new research papers.

5. A customer's distributed AI training job is experiencing poor performance. An analysis reveals high network latency. The architect suspects a misconfiguration in the NVIDIA Spectrum switches that form the RoCE-based AI fabric.
They review the following configuration snippet from one of the switch ports connected to a compute node:
```
interface ethernet 1/1
description "Connection to PCAI-Node-1"
speed 200G
mtu 9216
switchport mode trunk
## Priority Flow Control (PFC) is disabled on this port
```
What is the most likely cause of the performance issue?

A) Priority Flow Control (PFC) is disabled, which is mandatory for a lossless RoCE fabric.
B) The port speed is not sufficient for the connected H100 GPU.
C) The MTU size is too small for AI workloads.
D) The port is incorrectly configured in trunk mode instead of access mode.

Solutions: