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HPE Private Cloud AI Solutions Sample Questions:

1. A financial services firm is building a fraud detection application on HPE Private Cloud AI. The application needs to process a continuous stream of transaction data from multiple sources in real time.
The data science team requires a robust platform to build, manage, and execute the complex data pipelines needed to feed the AI model.
Which pre-integrated open-source tool within HPE AI Essentials is the industry standard for orchestrating and managing these complex data workflows and pipelines?

A) Kyverno
B) Prometheus
C) Apache Airflow
D) JupyterLab

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

3. A global logistics company is designing an enterprise-grade AI solution. The project has two main goals:
1. Goal 1: Develop a highly accurate, proprietary logistics optimization model by fine-tuning a foundation model on the company's massive, confidential shipping dataset (20TB). This requires a secure, high- performance, multi-node training environment.
2. Goal 2: Deploy a generative AI-powered chatbot for the customer service department. The chatbot must provide real-time shipment status and answer policy questions based on a knowledge base that is updated hourly.
The company is classified as an 'AI Pro,' with a formal AI strategy and a Center of Excellence, but they want a turnkey solution to accelerate time-to-market.
Which components and strategies should the architect propose to meet all the customer's requirements?
(Select all that apply.)
```
Customer Profile:
- Industry: Global Logistics
- AI Maturity: AI Pro
- Key Workloads: Large-scale Fine-Tuning, Real-time RAG
- Desired Solution: Turnkey, enterprise-grade private cloud
```

A) Rely solely on public cloud services for the fine-tuning job to avoid capital expenditure on high- performance GPUs.
B) Recommend that the customer build their own solution from individual components to have maximum control.
C) Use HPE AI Essentials to manage the training cluster, providing features like experiment tracking and optimized resource scheduling for the fine-tuning job.
D) Position Al-optimized HPE ProLiant DL servers at the edge for the fine-tuning workload to reduce data transfer costs.
E) Use the HPE Private Cloud AI Large configuration with NVIDIA H100 GPUs to provide the necessary performance for the large-scale fine-tuning task.
F) Implement a Retrieval-Augmented Generation (RAG) architecture for the customer service chatbot to ensure it uses the latest, hourly-updated information.

4. 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 an 'Early AI user'.
B) The customer is a 'Deployer of AI at scale'.
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 'AI Pro'.

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

Solutions: