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Title: Test C1000-185 Study Guide - C1000-185 Valid Test Pdf
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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q20-Q25):NEW QUESTION # 20
Your team is building a natural language processing pipeline using IBM watsonx components, where data from multiple external APIs and user inputs needs to be transformed, analyzed, and routed through various AI models. The process should involve the dynamic selection of models based on input data characteristics. The goal is to minimize latency while maintaining accuracy across tasks like sentiment analysis, text summarization, and query generation.
Which IBM watsonx service would you use to implement a flexible, model orchestration pipeline that meets these requirements, and why?
Answer: C

NEW QUESTION # 21
In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?
Answer: D

NEW QUESTION # 22
A client has deployed a generative AI model to generate technical support responses for various issues in their product line. To improve the accuracy of the responses, they have been using tuned prompts for each type of technical issue (e.g., network issues, hardware failures). However, the client is concerned about occasional misclassifications when the prompts are reused across similar but distinct issues. They want to optimize the prompts to handle edge cases better without sacrificing the response speed.
What approach would be most appropriate for managing and optimizing these tuned prompts?
Answer: D

NEW QUESTION # 23
You are developing a generative AI application using LangChain, and you want the system to perform actions like searching a database or retrieving live web content based on a user's request.
How can you best incorporate tools in LangChain to enable the AI to perform such tasks autonomously?
Answer: A

NEW QUESTION # 24
You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)
Answer: A,E

NEW QUESTION # 25
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