regardless of their relevance. B. Select only high-dimensional features to increase the complexity of the model and boost its predictive power. C. Use feature selection techniques to reduce dimensionality, enhancing model efficiency without sacrificing performance. D. Aggregate similar data types to minimize the need for feature selection during model optimization. Which of the following represents the most effective use of example input prompts within IBM Watsonx's Prompt Lab for generating a high -quality response?
A business wants to deploy a customer service chatbot using IBM watsonx, integrated with multiple back -end systems including ERP, CRM, and a payment gateway. To manage this complex integration, the chatbot should dynamically switch between these systems based on the customer’s intent. Which architecture best supports this requirement while ensuring scalability and minimal orchestration overhead?
entirely. C. Quantization may cause a significant drop in model accuracy, especially in embedding layers. Using quantization -aware training can help mitigate this. D. LLMs are inherently resistant to quantization, so switching to a smaller model architecture is the only viable solution. You are tasked with creating a prompt template for IBM Watsonx that will help generate product reviews for a new line of smartphones. The prompt needs to be adaptable across various product features and sentiment (positive, neutral, or negative) while maintaining a consistent structure. Which of the following prompt templates would be the most effective for generating well -structured, detailed reviews?
➢ TOTAL QUESTIONS: 379 In the context of IBM Watsonx and generative AI models, you are tasked with designing a model that needs to classify customer support tickets into different categories. You decide to experiment with both zero-shot and few -shot prompting techniques. Which of the following best explains the key difference between zero -shot and few -shot prompting?
stage, ensuring control over the process at every step. C. Use watsonx.ai to generate a summary immediately, and then perform NLP analysis and document retrieval in parallel to verify the accuracy of the output. D. Perform document retrieval first, followed by NLP analysis to extract relevant information, and then pass the processed data to watsonx.ai for summarization. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic -looking data without focusing on distribution conformity. Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson -Darling test?
impact across protected groups such as gender and race. B. Manually review all loan decisions generated by the model for signs of bias before releasing them to customers. C. Use greedy decoding in the inference phase to ensure deterministic outputs, avoiding potential bias from probabilistic sampling methods. D. Periodically retrain the model with updated datasets that exclude sensitive attributes such as gender and race. You are tasked with building a Retrieval -Augmented Generation (RAG) system for answering legal questions. The legal documents vary significantly in complexity and structure. How would you optimize embeddings in this domain to ensure the system retrieves the most relevant documents? (Select two)
