C1000-185 無料問題集「IBM watsonx Generative AI Engineer - Associate」

In the IBM watsonx Prompt Lab, you are tasked with improving a customer-facing chat model by editing and refining prompts.
Which of the following is an effective prompt editing option to fine-tune a chat-based generative AI model?

You are tuning a generative AI model to control the length of the generated responses.
Which of the following parameter configurations will ensure that the model generates responses that are at least 50 tokens long but no longer than 150 tokens?

When optimizing a model using soft prompts, which of the following is a potential advantage over hard prompts in the context of a generative AI application focused on creative content generation?

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?

In the context of generative AI, you are tasked with optimizing a model's performance for a variety of use cases by tuning the prompts. One of your colleagues mentions using a "soft prompt" to improve the model's adaptability.
What best describes the difference between a hard prompt and a soft prompt?

You are tasked with designing an AI prompt to extract specific data from unstructured text. You decide to use either a zero-shot or a few-shot prompting technique with an IBM Watsonx model.
Which of the following statements best describes the key difference between zero-shot and few-shot prompting?

Consider an organization implementing a RAG system to enhance the accuracy of their internal documentation search tool. The retriever is responsible for fetching relevant documents based on user queries.
What is the core capability of the retriever in this context?

You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.
Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?

In the context of a Retrieval-Augmented Generation (RAG) system, which type of retriever is best suited for retrieving documents based on semantic similarity in a vector space?

You are working on a large-scale enterprise application using IBM watsonx and need to ensure that different versions of your generative AI model prompts are properly managed for deployment.
Which of the following is the most appropriate action when planning the deployment of prompt versions?

You are tasked with deploying a foundation model for text generation on the IBM Watsonx platform. The foundation model has been pre-trained on a large corpus but has not been fine-tuned for your specific use case.
What is the most critical factor to consider when deploying this model to ensure it performs optimally on the Watsonx platform?

While generating synthetic data using IBM Watsonx's User Interface to supplement your fine-tuning dataset for a customer service chatbot, you notice some responses are incoherent or contain contradictions.
What steps should you take to maintain the quality and relevance of the synthetic data?

You are tasked with developing a customer support system for an e-commerce platform using the Retrieval-Augmented Generation (RAG) pattern. The system needs to retrieve relevant information from a large database of product specifications, user manuals, and FAQs. You decide to use LangChain for constructing the pipeline and SingleStore as the backend for storing and querying the document embeddings. The objective is to efficiently retrieve semantically similar documents and use them as input for a generative model that crafts human-like responses.
Which of the following steps best describes the correct implementation of the RAG pattern using LangChain and SingleStore for this customer support system?

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