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You are tasked with building a Retrieval-Augmented Generation (RAG) system using Elasticsearch for document storage, Watson ML for model hosting, and LangChain for orchestration. The chatbot is supposed to query a large database of medical records and generate responses based on the retrieved information. During testing, you notice that irrelevant documents are often retrieved, leading to low-quality responses.
What would be the best approach to improve document relevance in this RAG setup?
A. Fine-tune the Watson ML model on a dataset containing both queries and relevant documents, then update Elasticsearch with the model outputs.
B. Update the Elasticsearch index by re-indexing documents using embeddings from the Watson ML model.
C. Pre-process the medical records in LangChain to remove irrelevant information before sending the query to Elasticsearch.
D. Implement a BM25 similarity algorithm in Elasticsearch to improve document retrieval.
Answer: B
NEW QUESTION # 96
You are developing a document understanding system that integrates IBM watsonx.ai and Watson Discovery to extract insights from large sets of documents. The system needs to leverage watsonx.ai's large language model to summarize documents and Watson Discovery to search and extract relevant data from those documents.
What is the best approach to achieve this integration?
A. Use watsonx.ai's LLM to create a summary for each document in advance, and Watson Discovery only for searching pre-generated summaries.
B. Use Watson Discovery for summarizing documents and watsonx.ai's LLM for only retrieving relevant content from the documents.
C. Use watsonx.ai's LLM to both retrieve and summarize the documents, bypassing Watson Discovery.
D. Use Watson Discovery to index and search documents, and then send the retrieved documents to watsonx.ai's LLM for summarization through API calls.
Answer: D
NEW QUESTION # 97
You are tasked with deploying a custom Watsonx Generative AI model for a client who requires low-latency responses and scalability to handle unpredictable traffic.
Which deployment architecture would best meet the client's requirements?
A. Deploying the model on a single dedicated server
B. Using a serverless architecture with auto-scaling
C. Deploying the model in a local on-premise data center
D. Using a containerized architecture without orchestration
Answer: B
NEW QUESTION # 98
You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?
A. Zero-shot prompting, with the expectation that the model can summarize common concepts like AI due to pre-training.
B. Zero-shot prompting, but it requires the addition of a few examples for the model to generate a summary.
C. Zero-shot prompting, but the model may fail without detailed instructions on what aspects of the text to focus on.
D. Few-shot prompting, but the model will likely struggle without a few examples of summaries provided.
Answer: A
NEW QUESTION # 99
When addressing bias in a generative AI model, which of the following strategies is least likely to be effective in reducing biased outputs during text generation?
A. Leveraging prompt rephrasing techniques to remove bias-inducing keywords or phrases
B. Incorporating fairness constraints during the model's training phase
C. Training the model on a diverse and representative dataset
D. Using temperature control during generation to manage diversity in responses
Answer: D
NEW QUESTION # 100
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