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HPE Private Cloud AI Solutions Sample Questions:

1. An architect is in a discovery call with a customer who describes their project: "Our primary goal is to take our massive, proprietary dataset of chemical compound interactions and continuously update our foundational AI model's internal parameters to create a new, specialized model for drug discovery. This process runs 24/7 on a large GPU cluster." How should the architect classify this primary AI workload?

A) Retrieval-Augmented Generation (RAG)
B) AI Inferencing
C) Edge Computing
D) AI Model Training / Fine-tuning

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

3. In a Natural Language Processing (NLP) task, what is the role of a tokenizer?

A) To apply a final activation function to determine the output class.
B) To convert a sentence of raw text into a sequence of numerical tokens that a model can process.
C) To train the neural network using backpropagation.
D) To encrypt the input text to ensure data privacy during model training.

4. A customer states they need an AI solution for a "summarization" use case that will be accessed by approximately 75 concurrent users. The underlying data is updated frequently, so RAG will be required.
When using the HPE Intelligent Configurator, which three inputs are mandatory to get an initial sizing recommendation?

A) Use case, Number of users, RAG requirement
B) Use case, Model choice, Network switch preference
C) RAG requirement, Power and cooling capacity, Desired software license term
D) Number of users, Preferred GPU model, Data center location

5. 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?

A) Deployer of AI at scale
B) AI Curious
C) AI Pro
D) AI Beginner

Solutions: