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[General] Authentic C1000-185 Exam Braindumps present you first-grade Learning Guide - PDF

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【General】 Authentic C1000-185 Exam Braindumps present you first-grade Learning Guide - PDF

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q245-Q250):NEW QUESTION # 245
You are part of a team building an AI-powered assistant that helps software developers by answering technical queries. To handle the vast amount of technical documentation efficiently, the team has chosen to implement the Retrieval-Augmented Generation (RAG) pattern. LangChain is used to construct the retrieval-generation pipeline, and SingleStore is employed to store the document embeddings. You need to ensure that the integration of LangChain with SingleStore allows for real-time retrieval of relevant technical documentation.
Which of the following configurations should be used to ensure an optimal RAG implementation with LangChain and SingleStore?
  • A. Use LangChain to embed both queries and documents into the same vector space and store these embeddings in SingleStore. During inference, retrieve documents using SQL queries based on keyword similarity.
  • B. Use LangChain to retrieve pre-generated embeddings from SingleStore, and then fine-tune the generative model based on the retrieved embeddings to improve response accuracy.
  • C. Use LangChain to retrieve previously generated embeddings from SingleStore, then match these embeddings against the query using cosine similarity before passing them to the generative model.
  • D. Use LangChain to embed documents into a vector space and store these embeddings in SingleStore. During inference, embed the query in the same vector space and retrieve documents using vector similarity search.
Answer: D

NEW QUESTION # 246
You are working on a Retrieval-Augmented Generation (RAG) system to enhance the performance of a generative model. The RAG model needs to leverage a document corpus to generate answers to complex questions.
Which of the following steps is critical in the RAG pipeline to ensure accurate and relevant answer generation?
  • A. Fine-tuning the generative model on the entire document corpus without retrieval components.
  • B. Using keyword-based search to retrieve documents and then allowing the generative model to synthesize answers from those documents.
  • C. Retrieving only the longest document in the corpus as the generative model can synthesize information more effectively from detailed content.
  • D. Indexing the document corpus using embeddings, retrieving relevant documents, and feeding them as context into the generative model.
Answer: D

NEW QUESTION # 247
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. Train Watson Assistant to handle both structured and unstructured conversations, while using watsonx.ai only for rare edge cases that Watson Assistant cannot manage.
  • B. Create separate chat interfaces for Watson Assistant and watsonx.ai, and allow the user to choose which system to query based on their needs.
  • C. Use Watson Discovery to preprocess all queries before routing them to either Watson Assistant or watsonx.ai based on query complexity.
  • 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 # 248
You are fine-tuning a generative model to generate text-based responses in a customer service chatbot. You want to ensure the responses are concise and relevant, without causing the model to produce overly long or irrelevant output.
Which of the following parameters and stopping criteria would be most effective for achieving this goal?
  • A. Increase the temperature to 1.5 and set a high maximum token limit.
  • B. Use beam search decoding with a low beam width and a high repetition penalty.
  • C. Set a low top-k value and implement a repetition penalty with a low maximum token limit.
  • D. Use greedy decoding with no repetition penalty and a high stopping probability threshold.
Answer: C

NEW QUESTION # 249
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?
  • A. IBM watsonx API Gateway to handle external data inputs, route them to different models, and ensure that each input is preprocessed in a low-latency manner.
  • B. IBM watsonx Orchestrator, which allows for the integration and management of multiple AI models and can dynamically route inputs to the appropriate model based on predefined criteria.
  • C. IBM watsonx Data Refinery, as it can preprocess and analyze incoming data, and use its rules- based engine to route it to different models.
  • D. IBM watsonx Model Management to dynamically select and orchestrate the models for different tasks based on real-time data analysis.
Answer: B

NEW QUESTION # 250
......
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