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HP HPE2-B08 Exam Syllabus Topics:

SectionObjectives
AI Infrastructure Design- Compute and GPU considerations
- Networking for AI workloads
- Storage and data pipeline design
Security in Private Cloud AI- Workload and data protection
- Identity and access management
Deployment and Operations- Deployment models for AI solutions
- Lifecycle management of AI infrastructure
- Monitoring and optimization
HPE Private Cloud AI Fundamentals- Core AI workload characteristics
- Overview of private cloud AI concepts
Data Management and Governance- Data governance and compliance
- Data lifecycle management
HPE GreenLake for AI Solutions- GreenLake architecture and services
- Consumption-based IT model for AI

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Latest HPE2-B08 Questions - Certification HPE2-B08 Exam Infor

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HPE Private Cloud AI Solutions Sample Questions (Q83-Q88):

NEW QUESTION # 83
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.)

Answer: B,E


NEW QUESTION # 84
A customer has used the HPE Intelligent Configurator and determined that the HPE Private Cloud AI
"Small - Expanded" configuration meets their needs. They now need to generate a final, quotable Bill of Materials (BOM).
What is the most direct and efficient method for the sales team to create this BOM?

Answer: C


NEW QUESTION # 85
An IT director for a regional retail chain tells you they are "doing AI." Upon further questioning, you learn they have one data scientist who has built a single, experimental sales forecasting model as a proof-of-concept (PoC). The project lacks clear KPIs for success and there is no formal strategy for how to productionize it or what to do next.
How would you classify this customer's AI maturity level?

Answer: C


NEW QUESTION # 86
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.)

Answer: B,C,E


NEW QUESTION # 87
An architect is evaluating the high operational costs associated with their company's internal AI platform. The primary workload involves fine-tuning a 70-billion parameter LLM for various departmental tasks. The team reports that the GPU cluster utilization is low, and jobs often fail, requiring manual restarts.
They are reviewing the platform's configuration:
```
- Model: Llama 2 70B
- Task: Supervised Fine-Tuning
- Cluster Size: 16x nodes, each with 4x NVIDIA A100 GPUs
- Scheduling: Manual job submission via SSH scripts
- Data Management: Datasets manually copied to local storage on each node
- Collaboration: Code and models shared via a central Git repository
```
Which NVIDIA AI Enterprise component is specifically designed to address the challenges of low GPU utilization and manual job management in a multi-node training environment like this?

Answer: A


NEW QUESTION # 88
......

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