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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are developing a natural language generation (NLG) system for financial report summaries. The system needs to generate concise, high-quality summaries for different financial instruments. Tuning Studio is available as a tool to improve the performance of the model.
Which of the following capabilities of Tuning Studio is most helpful for improving the NLG system's performance in this domain-specific application?

A) It enables the fine-tuning of pre-trained models on custom datasets to improve accuracy and relevance to the specific domain.
B) It provides real-time feedback on model performance during text generation.
C) It allows you to visually inspect and analyze the model's token-by-token generation process.
D) It helps in selecting the most relevant datasets from a pre-existing library for fine-tuning the model.

2. Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

A) To perform entity recognition and classify documents based on specific keywords, ignoring the overall document meaning.
B) To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.
C) To return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
D) To generate new responses based on training data, without relying on external data sources.

3. In the context of Retrieval-Augmented Generation (RAG) models, embeddings play a crucial role in retrieving relevant documents.
Which of the following best describes the purpose of embeddings in GenAI, particularly within a RAG system?

A) Embeddings are used to compress large documents into a smaller format, enabling faster storage and retrieval within a RAG system.
B) Embeddings represent each word in a fixed-length vector based on its semantic meaning, which allows for precise retrieval of documents by directly matching words between the query and the documents.
C) Embeddings are primarily used to measure the grammatical correctness of the generated responses by checking the sentence structure in the generated text.
D) Embeddings are dense vector representations of words, phrases, or entire documents that capture semantic relationships, making it easier to find similar content in the knowledge base during retrieval.

4. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
B) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
C) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
D) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.
E) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.

5. You are implementing a RAG system and have chosen LlamaIndex to handle the document indexing process. Your system needs to retrieve relevant documents quickly and efficiently for large datasets.
What is the most important function of LlamaIndex in managing document retrieval?

A) LlamaIndex compresses the documents and stores them in a traditional SQL database to improve retrieval speed.
B) LlamaIndex generates summaries of documents and uses these summaries for quick retrieval rather than the full document.
C) LlamaIndex creates keyword-based indexes of documents, optimizing for exact word matches rather than semantic search.
D) LlamaIndex transforms documents into high-dimensional embeddings and stores them in a vector database to enable fast semantic search.

Solutions: