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HP HPE2-B08 Exam Syllabus Topics:

HPE Private Cloud AI Solutions Sample Questions:

1. 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 network switches are not configured for lossless operation (e.g., PFC is disabled).
B) The NVIDIA peer memory driver has not been installed on the guest VM.
C) The NVIDIA GPUs have been configured with Multi-Instance GPU (MIG), which enhances GDS performance.
D) The HPE GreenLake for File Storage array is using SATA SSDs instead of NVMe SSDs.
E) The application is using a standard TCP/IP socket for data transfer instead of an RDMA-based library.

2. An architect is meeting with a prospective customer to determine the right HPE AI solution. The customer provides the following information about their situation.
```
- AI Status: No formal AI strategy. One successful PoC for theft prevention using computer vision is running at a single store.
- Goal: Wants to explore creating a "digital twin" of their supply chain for simulation, but has no clear KPIs.
- Team: Two data scientists, one ML engineer.
- Infrastructure: Ad-hoc use of public cloud for the PoC; no standardized tech stack.
```
Based on this profile, which AI maturity level and corresponding HPE solution should the architect initially position? (Choose 2.)

A) The customer is a 'Deployer of AI at scale'.
B) The customer is an 'AI Pro'.
C) Lead with HPE Cray systems for the digital twin simulation.
D) Lead with HPE Private Cloud AI with NVIDIA.
E) The customer is an 'Early AI user'.

3. A customer who is an "AI Beginner" wants to start an AI inferencing project at their edge locations.
Their goal is to analyze security camera feeds to help prevent theft. They have a limited budget and IT staff at the edge sites.
Which HPE AI solution is the most appropriate to position for this specific scenario?

A) An HPE AI Services - Transformation Workshop
B) AI-optimized HPE ProLiant DL servers
C) HPE Cray systems
D) NVIDIA DGX Systems
E) HPE Private Cloud AI with NVIDIA

4. An enterprise wants to deploy pre-trained foundation models from various sources for multiple business units. A key requirement is to simplify and standardize the deployment process, regardless of the model's origin. They want a solution that packages models into scalable, optimized, and easy-to-use microservices with a standard API.
Which component of the NVIDIA AI Enterprise software suite directly addresses this need?

A) NVIDIA NIM (NVIDIA Inference Microservices)
B) NVIDIA Triton Inference Server
C) NVIDIA NeMo
D) NVIDIA TAO Toolkit

5. An architect is assessing a customer's workloads to position the correct HPE Private Cloud AI configuration. The customer has a single, massive task: training a foundational vision model from scratch using a petabyte-scale image dataset. The project is expected to take months and requires the absolute maximum compute performance available.
Which AI workload type does this represent, and which HPE solution is most appropriate?

A) Workload: AI Inferencing; Solution: AI-optimized HPE ProLiant DL servers
B) Workload: Edge Computing; Solution: HPE ProLiant DL320 server
C) Workload: RAG; Solution: HPE Private Cloud AI - Medium configuration
D) Workload: AI Model Training; Solution: HPE Cray Systems

Solutions: