You are developing a legal research assistant that uses Retrieval-Augmented Generation (RAG) to help lawyers find relevant case law. The system must provide case law that is not only relevant to the query but also semantically similar in meaning to the legal terminology used.
Given this scenario, which of the following retrievers would be the most appropriate choice for this application?

• A. A sparse retriever based on keyword matching and BM25 algorithm.
• B. A rule-based retriever optimized for simple, pre-defined keyword mappings.
• C. A dense retriever based on embeddings from a fine-tuned BERT model.
• D. A sparse retriever that integrates exact matching of legal terms with synonym matching.

Which of the following techniques is the most effective for reducing bias in generative AI models through prompt engineering?

• A. Allowing the model to auto-correct its own responses by cross-referencing with other outputs
• B. Using neutral and carefully phrased prompts to avoid triggering biased outputs
• C. Fine-tuning the model using training data that explicitly includes examples of biased outputs
• D. Applying Greedy Decoding to ensure the most likely tokens are selected during generation

You are tasked with optimizing a generative AI model's usage in a chatbot that provides troubleshooting instructions for software issues. The current prompt template is:
"Please provide step-by-step troubleshooting instructions for the following issue: [Issue Description]. Be detailed, include specific commands or settings the user should check, and provide potential reasons for failure." To reduce the token count and ensure cost efficiency, which of the following prompt template modifications would best manage token usage while preserving essential information?

• A. "Provide step-by-step instructions for troubleshooting the issue: [Issue Description]. Include commands and reasons for failure."
• B. "Give troubleshooting instructions for [Issue Description], including steps, commands, and reasons for failure."
• C. "Troubleshoot the following issue: [Issue Description]. Offer step-by-step commands and reasons for failure."
• D. "Provide detailed troubleshooting instructions for the issue: [Issue Description], with steps, commands, and potential reasons for failure."

Your team has developed multiple versions of a custom Watsonx Generative AI model to address different use cases. During deployment, the client requires that all model versions be deployed concurrently, allowing requests to be routed to the appropriate model based on the input data.
What deployment strategy would best accommodate this requirement?

• A. Blue-Green deployment
• B. Canary deployment
• C. A/B testing deployment
• D. Multi-model serving with dynamic routing

You are designing a Retrieval-Augmented Generation (RAG) system that will handle real-time queries from users, using a combination of a retriever and a transformer-based generator.
Which of the following implementation details is the most critical to ensure that the system delivers responses in a timely manner while maintaining accuracy?

• A. The retriever should use exact match algorithms to minimize retrieval time and complexity.
• B. The retriever and generator should operate independently of each other to avoid communication overhead.
• C. The system should cache previous queries and responses to avoid invoking the retriever and generator multiple times.
• D. The retriever should utilize an efficient vector search algorithm with approximate nearest neighbor (ANN) techniques to balance speed and accuracy.