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HPE Private Cloud AI Solutions Sample Questions:

1. An architect is presenting to a customer who is an 'Early AI User'. The customer is unsure about the differences between their two main project ideas.
Project 1: Create an internal chatbot that can answer HR policy questions by accessing the live employee handbook stored on a shared drive.
Project 2: Teach a general-purpose chatbot to adopt the company's formal communication style and tone for drafting official press releases.
How should the architect categorize the primary AI task for each project? (Choose 2.)

A) Project 2 is a fine-tuning workload.
B) Project 1 is a model training workload.
C) Project 1 is a RAG (Retrieval-Augmented Generation) workload.
D) Project 2 is a classic inferencing workload with no model customization.

2. An 'AI Pro' customer wants to deploy a solution to create 'digital twins' of their manufacturing equipment for predictive maintenance simulations. This workload requires significant GPU power for both the simulation and the underlying AI model.
Which HPE solution should be positioned as the primary platform for this advanced, data-center based workload?

A) Individual HPE ProLiant DL320 Gen11 servers at the edge.
B) A standard data warehouse solution without GPU acceleration.
C) HPE Private Cloud AI with NVIDIA.
D) The customer's existing VDI environment.

3. An architect uses the HPE Intelligent Configurator for a customer with 120 concurrent users for a text generation task with RAG. The tool recommends a "Medium - Expanded (4-node)" configuration. The customer then reveals they want to use a smaller, more efficient 7B parameter model instead of the 13B model the tool defaulted to.
How will this change in model size likely affect the sizing tool's recommendation?

A) It will require the architect to switch to the "application capacity" sizing method.
B) It will downgrade the recommendation to a "Medium - Standard (2-node)" configuration.
C) It will have no effect, as the number of users is the primary factor.
D) It will upgrade the recommendation to a "Large - Standard (4-node)" configuration.

4. An enterprise architecture team is debating the best method to adapt a general-purpose Large Language Model (LLM) for two different, highly-specialized internal use cases:
1. Use Case A: A customer support chatbot that must provide answers strictly based on a rapidly changing knowledge base of product manuals and technical notes. Verifiability and traceability of the information source are critical.
2. Use Case B: An internal code generation assistant that needs to learn the company's specific coding style, proprietary frameworks, and API usage patterns from a large, static codebase.
Which are the most appropriate strategies for these use cases? (Choose 2.)

A) Use fine-tuning for Use Case B to embed the company-specific coding patterns and styles into the model's behavior.
B) Use both RAG and fine-tuning for both use cases as they are always used together.
C) Use Retrieval-Augmented Generation (RAG) for Use Case A to provide up-to-date, verifiable information at inference time.
D) Use fine-tuning for Use Case A to ensure the model deeply learns the product manual content.
E) Use RAG for Use Case B to allow the model to retrieve code snippets from the static codebase.

5. 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) Prometheus
B) Apache Airflow
C) Kyverno
D) JupyterLab

Solutions: