C1000-185 NEW BRAINDUMPS FILES, C1000-185 PRACTICE EXAM QUESTIONS

C1000-185 New Braindumps Files, C1000-185 Practice Exam Questions

C1000-185 New Braindumps Files, C1000-185 Practice Exam Questions

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Tags: C1000-185 New Braindumps Files, C1000-185 Practice Exam Questions, C1000-185 Associate Level Exam, C1000-185 Reliable Exam Syllabus, C1000-185 Examcollection Dumps

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q205-Q210):

NEW QUESTION # 205
You are tasked with implementing a Retrieval-Augmented Generation (RAG) pipeline for a customer service chatbot. Your goal is to ensure that the model can query a vast knowledge base, retrieve relevant documents, and generate responses that reference these documents. You are using the transformers and faiss libraries in Python to achieve this. After retrieving the relevant passages, the generative model will then process them to formulate a final response.
What is the most appropriate sequence of steps for implementing this RAG pipeline?

  • A. Generate the response first using the transformer model -> Retrieve documents from the knowledge base -> Encode the documents using FAISS -> Use the retrieved documents to fine-tune the generated response.
  • B. Use the FAISS library to create an index -> Encode the input query using the transformer model -> Retrieve the most relevant documents -> Pass the retrieved documents to the generative model for response generation.
  • C. Initialize the generative model -> Use FAISS to encode the retrieved documents -> Pass the encoded documents to the transformer model for response generation.
  • D. Encode the query using FAISS -> Use the transformer model to retrieve documents from the knowledge base -> Generate a response using the encoded documents.

Answer: B


NEW QUESTION # 206
When deploying AI assets in a deployment space, what is the most critical benefit of using deployment spaces in a large-scale enterprise environment?

  • A. Better data labeling quality through automated labeling tools
  • B. Isolated environments to manage and monitor multiple model versions
  • C. Improved model accuracy through hyperparameter tuning
  • D. Faster training times due to streamlined compute resources

Answer: B


NEW QUESTION # 207
You are working on enhancing the search functionality in a customer service chatbot by implementing the Retrieval-Augmented Generation (RAG) pattern. The chatbot needs to answer customer queries about various technical issues by retrieving relevant information from a knowledge base. Your team is discussing different ways to structure the RAG system and how to implement the pattern efficiently using existing tools.
Which of the following statements best describes the RAG pattern, and how it should be implemented in the context of this chatbot?

  • A. The RAG pattern enhances a generative model by retrieving relevant documents, which are then used as context for generating a final response.
  • B. The RAG pattern prioritizes generating answers based on the frequency of document appearances in the retrieval phase, improving precision.
  • C. The RAG pattern combines dense retrieval with a vector store, where retrieved documents are directly presented as final answers.
  • D. The RAG pattern integrates sparse retrieval with a rule-based system for generating responses based on exact document matches.

Answer: A


NEW QUESTION # 208
In the context of prompt engineering, how does the repetition penalty parameter influence the behavior of a generative AI model, and which of the following scenarios demonstrates its correct usage?

  • A. A repetition penalty of 0.5 is ideal for generating creative text, as it promotes repetition of key phrases for emphasis
  • B. A repetition penalty higher than 1.0 will discourage the model from repeating the same words or phrases, while a value lower than 1.0 encourages repetition
  • C. A repetition penalty of 1.0 encourages the model to introduce more varied word choices in its output
  • D. Setting the repetition penalty to 1.0 disables any influence on repetition and allows the model to generate text without penalizing previously used tokens

Answer: B


NEW QUESTION # 209
You are working on a project that requires generating a large volume of product descriptions for an e-commerce website. The descriptions must be unique, creative, and optimized for SEO. The client has specified that the descriptions must also include certain technical product specifications, but they should not be overly mechanical or robotic in tone. Your team has access to an LLM pre-trained on large-scale, general-purpose corpora.
Based on this scenario, what would be your first step to design the most effective Generative AI solution for this task?

  • A. Use the pre-trained LLM directly without any modification and feed it the product specifications, prompting it to generate descriptions.
  • B. Optimize the generation process using greedy decoding to ensure concise and accurate descriptions.
  • C. Fine-tune the pre-trained LLM on a domain-specific dataset of e-commerce product descriptions with an emphasis on SEO-friendly language.
  • D. Use prompt engineering to add technical specifications dynamically to generated text without fine-tuning the model.

Answer: C


NEW QUESTION # 210
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