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HPE Private Cloud AI Solutions Sample Questions:
1. A customer is using the HPE Intelligent Configurator to determine the correct size for an HPE Private Cloud AI solution. Their primary use case is a text generation chatbot for internal HR support.
What is the MOST critical piece of information the customer must provide to the sizing tool to get an accurate recommendation for the number and type of GPUs required?
A) The physical location of the data center where the solution will be deployed.
B) The estimated number of concurrent users who will be querying the chatbot.
C) The brand of networking switches used in their existing data center fabric.
D) The number of ML engineers who will maintain the model.
2. A large financial institution, a known "Deployer of AI at scale," needs to train a next-generation fraud detection model. This new model has over a trillion parameters, significantly larger than their current models, and requires an exascale-class computing solution to be trained in a reasonable timeframe.
Which HPE AI solution should be positioned to meet this customer's demanding requirement?
A) HPE Cray systems
B) HPE GreenLake for File Storage
C) HPE Private Cloud AI with NVIDIA, Large configuration
D) AI-optimized HPE ProLiant DL servers
3. A data analytics team is running workloads on an HPE Private Cloud AI solution. They observe that a data ingestion job is not meeting performance expectations, suspecting a CPU bottleneck. They believe the application is not correctly leveraging GPUDirect Storage (GDS), forcing data to be copied through the server's main memory before reaching the GPU.
Which are valid reasons why GDS might not be functioning correctly? (Choose 3.)
A) The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
B) The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.
C) The network switches are not configured for lossless operation (e.g., PFC is disabled).
D) The NVIDIA peer memory driver has not been installed on the guest VM.
E) The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
4. A customer in the public sector wants to use AI to analyze live video feeds from city-wide cameras to automate public safety tasks like detecting traffic accidents or identifying security threats in real-time.
This is their first major AI initiative.
Which AI use case does this scenario represent?
A) Natural Language Processing (NLP)
B) Computer Vision / Intelligent Video Analytics
C) Drug Discovery
D) Recommender Systems
5. A customer wants to enhance their existing Large Language Model (LLM) to provide more accurate and contextually relevant answers based on a proprietary, rapidly changing knowledge base of legal documents. They are considering two approaches: fine-tuning and Retrieval-Augmented Generation (RAG).
Review the data flow diagram for the proposed RAG implementation:
```
User Query -> [Query Encoder] -> Vector DB Search -> [Retrieved Documents] --+
|
+-> [LLM Prompt] -> LLM -> Response
```
Based on the diagram and the scenario, which statement accurately identifies a primary advantage of the RAG approach for this customer?
A) RAG requires retraining the LLM whenever a new legal document is added to the knowledge base.
B) RAG permanently modifies the LLM's internal weights to specialize in legal terminology.
C) RAG reduces the need for a vector database by directly integrating documents into the model.
D) RAG allows the LLM to access the most current legal documents at inference time without daily retraining.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: B,C,D | Question # 4 Answer: B | Question # 5 Answer: D |






