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You are designing a customer support chatbot using watsonx.ai as the primary generative model. You want to enhance the chatbot's capabilities by integrating it with IBM Watson Assistant to handle structured conversations while allowing watsonx.ai to generate responses for open-ended queries.
Which integration approach would most effectively combine both services while maintaining optimal performance and accuracy?
A. Create separate chat interfaces for Watson Assistant and watsonx.ai, and allow the user to choose which system to query based on their needs.
B. Use Watson Discovery to preprocess all queries before routing them to either Watson Assistant or watsonx.ai based on query complexity.
C. Train Watson Assistant to handle both structured and unstructured conversations, while using watsonx.ai only for rare edge cases that Watson Assistant cannot manage.
D. Implement Watson Assistant for structured conversations and use a middleware layer that dynamically routes complex, open-ended queries to watsonx.ai, returning the results within the same session.
Answer: D
NEW QUESTION # 111
You are tasked with deploying two generative AI systems: one for document summarization and another for question-and-answer (Q&A). Both systems need to handle large volumes of unstructured text, but they differ in their response time requirements and interaction with users.
Which deployment strategy would be most appropriate for this scenario?
A. Deploy the summarization model as a batch processing system, while the Q&A system is deployed as an on-demand service using a low-latency inference model.
B. Deploy both systems as a single pipeline, with the summarization model providing input to the Q&A model to improve the quality of answers.
C. Use a single deployment strategy where both the summarization and Q&A systems are integrated into a shared model that processes all requests through the same inference pipeline.
D. Use a rule-based model for summarization and a machine learning-based model for Q&A, combining them into a single service endpoint for deployment.
Answer: A
NEW QUESTION # 112
You are designing prompt templates for an automated content generation tool that your team uses for marketing copy. The tool must balance cost efficiency with the generation of high-quality outputs. The prompts should be structured to avoid excessive token usage while ensuring that the model provides coherent and actionable content.
Which of the following techniques would best optimize the design of cost-effective prompt templates?
A. Designing prompts that incorporate redundancy to ensure model outputs address all possible user needs.
B. Creating templates that provide extensive background information to give the model context.
C. Using placeholders in the prompt templates for dynamic insertion of variables specific to each task.
D. Embedding explicit stop words within the prompt to control the length of generated outputs.
Answer: C
NEW QUESTION # 113
Which of the following is the most effective approach when planning for data elements to optimize application usage in IBM watsonx generative AI models?
A. Select only high-dimensional features to increase the complexity of the model and boost its predictive power.
B. Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance.
C. Ensure that all features are included in the model to capture as much data context as possible, regardless of their relevance.
D. Aggregate similar data types to minimize the need for feature selection during model optimization.
Answer: B
NEW QUESTION # 114
A company is using a Retrieval-Augmented Generation (RAG) system to enhance its question-answering model by incorporating relevant documents from a knowledge base. They need to generate vector embeddings for these documents and the queries using a pretrained transformer-based model.
What is the most crucial aspect of generating vector embeddings for effective retrieval in this scenario?
A. Use a different transformer model for document embeddings and query embeddings to ensure diversity in representations.
B. Ensure that both documents and queries are encoded using the same embedding model.
C. Generate vector embeddings with a static embedding model that does not account for fine-tuning on the specific dataset.
Answer: B
NEW QUESTION # 115
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