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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q256-Q261):
NEW QUESTION # 256
You are implementing a Retrieval-Augmented Generation (RAG) system using IBM Watsonx to improve your generative AI model. The system retrieves relevant information from a large corpus and augments it into the generative process.
In this context, what role do embeddings play in a RAG-based system?
- A. Embeddings are used to retrieve relevant documents by calculating the semantic similarity between user queries and the stored documents.
- B. Embeddings ensure that only syntactically correct documents are retrieved, without regard to semantic content.
- C. Embeddings reduce the size of the generative model by compressing the parameters into a smaller representation.
- D. Embeddings store the entire content of documents, which is then directly passed to the generative model.
Answer: A
NEW QUESTION # 257
You are tasked with designing a prompt for a sentiment analysis model based on a large language model (LLM). The goal is to generate a coherent response from the model that aligns with a particular sentiment (positive, negative, or neutral) for customer reviews of a product.
Which of the following prompt designs are best suited to generate a positive review response? (Select two)
- A. "Analyze the product based on the customer feedback and write a review that covers all sentiments."
- B. "Describe the product as if you were a very satisfied customer, and you were recommending it to a friend."
- C. "Write a neutral review, neither praising nor criticizing the product."
- D. "Write a review about the product that highlights both its pros and cons."
- E. "Generate a positive review about the product, focusing on the key strengths and avoiding any negative aspects."
Answer: B,E
NEW QUESTION # 258
You are designing a Retrieval-Augmented Generation (RAG) model within IBM watsonx to assist in generating responses for a customer service chatbot. The model needs to leverage a knowledge base (KB) of articles to enhance the accuracy of responses.
Which of the following correctly describes how the RAG model can be implemented to achieve this goal?
- A. The RAG model retrieves entire documents from the knowledge base and directly outputs them as the response to the user's query.
- B. The RAG model first generates a response from the language model and then retrieves related articles from the knowledge base to augment the output.
- C. The RAG model retrieves relevant knowledge from the knowledge base using a search or retrieval mechanism before generating a response, which is then influenced by this retrieved information.
- D. The RAG model first generates an intermediate query based on the input question, which is then passed through a retrieval system to generate the final response.
Answer: C
NEW QUESTION # 259
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 return relevant documents or passages from a knowledge base by calculating semantic similarity between the query and documents.
- C. To generate new responses based on training data, without relying on external data sources.
- D. To perform pre-defined template matching on queries to retrieve documents that exactly match pre-configured templates.
Answer: B
NEW QUESTION # 260
A financial institution is using a generative AI model to create reports based on transaction data. During deployment, the institution notices that the model sometimes fabricates trends or patterns that do not exist in the underlying data. This is an example of a hallucination.
Which of the following techniques would best minimize this risk during inference?
- A. Reduce the model size to decrease its capacity to hallucinate complex patterns.
- B. Disable the model's autoregressive capability to prevent it from generating future predictions.
- C. Use a retrieval-augmented generation (RAG) model that incorporates external financial data into the generation process.
- D. Increase the top-p value to ensure more tokens are considered during generation.
Answer: C
NEW QUESTION # 261
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