DOWNLOAD REAL IBM C1000-185 PRACTICE TEST QUESTIONS AND START PREPARATION

Download Real IBM C1000-185 Practice Test Questions And Start Preparation

Download Real IBM C1000-185 Practice Test Questions And Start Preparation

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IBM watsonx Generative AI Engineer - Associate Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are implementing a Retrieval-Augmented Generation (RAG) system using LangChain, IBM WatsonX, and a vector database. The system needs to answer complex technical questions by retrieving relevant technical documents and generating a coherent response.
Which of the following best describes LangChain's role in the RAG pattern implementation?

  • A. LangChain is responsible for storing and retrieving documents using a sparse keyword-based retriever from a traditional relational database.
  • B. LangChain optimizes document retrieval by pre-generating responses based on common queries and caching them in memory for future use.
  • C. LangChain serves as an intermediary, facilitating communication between the vector database and WatsonX's LLM, ensuring that retrieved documents are contextually relevant for the LLM's response generation.
  • D. LangChain allows WatsonX's LLM to fine-tune its parameters based on user interactions to improve future retrieval and generation accuracy.

Answer: C


NEW QUESTION # 72
After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

  • A. Large difference between training and validation accuracy
  • B. Low validation loss
  • C. Small difference between training and validation loss
  • D. Low training loss
  • E. High training accuracy

Answer: B,C


NEW QUESTION # 73
You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.
Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

  • A. Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
  • B. Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.
  • C. Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.
  • D. Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.

Answer: D


NEW QUESTION # 74
You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?

  • A. Running evaluation metrics on the baseline model to measure initial performance.
  • B. Deploying the model in a real-time environment for user feedback collection.
  • C. Defining task-specific instructions and fine-tuning them through prompt design.
  • D. Pre-processing and augmenting the training data to improve the model's generalization capabilities.

Answer: C


NEW QUESTION # 75
You are building a question-answering system using a Retrieval-Augmented Generation (RAG) architecture. You are deciding whether to incorporate a vector database into the system to handle the document embeddings.
Under which of the following circumstances is the use of a vector database most appropriate?

  • A. When the text corpus consists entirely of predefined categories that can be handled by simple keyword matching algorithms
  • B. When real-time similarity search over high-dimensional embeddings is needed for large-scale unstructured text data
  • C. When the data consists primarily of binary files such as images and videos, and full-text search is required
  • D. When the corpus consists mainly of short, structured text like JSON records and traditional SQL indexing will suffice

Answer: B


NEW QUESTION # 76
......

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