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[General] Test C1000-185 Study Guide - C1000-185 Valid Test Pdf

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【General】 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?
  • A. IBM watsonx Model Management to dynamically select and orchestrate the models for different tasks based on real-time data analysis.
  • B. IBM watsonx Data Refinery, as it can preprocess and analyze incoming data, and use its rules- based engine to route it to different models.
  • C. 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.
  • D. 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.
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?
  • A. Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.
  • B. Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
  • C. Embeddings are used to directly generate the textual responses in the output.
  • D. Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
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?
  • A. Create highly specific prompts for each possible issue, fine-tune the model on each prompt, and prioritize correctness over speed.
  • B. Use a single general prompt, apply greedy decoding, and manually edit outputs for any misclassifications.
  • C. Use a single tuned prompt for each product category, apply top-p sampling, and rely on post-processing to correct any misclassifications.
  • D. Leverage prompt chaining, where an initial general prompt narrows down the issue type, followed by a more specific tuned prompt for the final response.
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?
  • A. Use a LangChain agent with a predefined set of tools to dynamically select and invoke the appropriate tool (e.g., database access, API call) based on the user's request.
  • B. Build a LangChain chain that uses user inputs to sequentially call all the available tools and pick the one with the most relevant output.
  • C. Configure LangChain to automatically load data from static sources based on historical query patterns, avoiding the need for dynamic tool selection.
  • D. Rely on LangChain's memory module to remember previous user queries and provide real-time data access.
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)
  • A. Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
  • B. Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
  • C. Generate only a small amount of synthetic data to minimize computational costs and training time.
  • D. Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
  • E. Ensure that the synthetic data covers edge cases as well as common industry scenarios.
Answer: A,E

NEW QUESTION # 25
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